Google’s Gemini: The Next Generation of AI
Google is making waves in the world of artificial intelligence with the introduction of Gemini, its flagship suite of generative AI models, applications, and services. Gemini represents a significant step forward in AI technology, offering advanced capabilities and multimodal functionality.
Understanding Gemini
Gemini comes in three distinct versions, each tailored to specific use cases and performance requirements:
- Gemini Ultra: The most powerful and feature-rich Gemini model.
- Gemini Pro: A lightweight version of Gemini, designed for more efficient processing.
- Gemini Nano: A compact model optimized for mobile devices, such as the Google Pixel smartphone series.
What sets Gemini apart from other AI models is its native multimodality. Unlike models like LaMDA, which was trained solely on text data, Gemini models can understand and generate various forms of media, including audio, images, videos, and code, in addition to text in multiple languages.
Gemini Apps vs. Gemini Models
Image Credits: Google
It’s important to note that Gemini apps (formerly known as Bard) and Gemini models are distinct entities. The Gemini apps serve as an interface for accessing certain Gemini models, acting as a client for Google’s generative AI technology. These apps and models are also separate from Imagen 2, Google’s text-to-image model available in some of the company’s development tools and environments.
The Potential of Gemini
Thanks to its multimodal capabilities, Gemini has the potential to revolutionize various tasks, from speech transcription and image captioning to artwork generation. While not all of these features have been fully realized in products yet, Google promises to deliver them in the near future.
Gemini Ultra: The Powerhouse
Gemini Ultra, the most advanced model in the Gemini family, offers a wide range of applications. It can assist with complex tasks like physics homework, solving problems step-by-step, and identifying errors in pre-filled answers. Additionally, Gemini Ultra can help researchers identify relevant scientific papers, extract information, and even update charts with more recent data by generating the necessary formulas.
While Gemini Ultra technically supports image generation, this feature has not yet been integrated into the productized version of the model. Unlike apps like ChatGPT, which rely on separate image generators like DALL-E, Gemini outputs images natively, without an intermediary step.
Gemini Ultra is accessible through the Vertex AI API, Google’s fully managed AI developer platform, and AI Studio, a web-based tool for app and platform developers. It also powers the Gemini apps, but access to Gemini Ultra through Gemini Advanced requires a subscription to the Google One AI Premium Plan, priced at $20 per month. This plan also integrates Gemini with Google Workspace, allowing users to summarize emails, capture notes during video calls, and more.
Gemini Pro: Enhanced Reasoning and Understanding
Gemini Pro represents an improvement over LaMDA in terms of reasoning, planning, and understanding capabilities. An independent study conducted by researchers from Carnegie Mellon University and BerriAI found that the initial version of Gemini Pro outperformed OpenAI’s GPT-3.5 in handling longer and more complex reasoning chains.
However, like all large language models, Gemini Pro has its limitations. The study revealed that it struggled with mathematics problems involving multiple digits, and users have discovered instances of flawed reasoning and obvious mistakes.
Google seriously underdelivered with the original Bard launch. And more recently it ruffled feathers with a video purporting to show Gemini’s capabilities that turned out to have been heavily doctored and was more or less aspirational.
Despite these challenges, Google remains committed to refining and improving the Gemini models, with the goal of delivering cutting-edge AI technology that can transform the way we work, learn, and interact with information.
Google’s Gemini AI: A Comprehensive Overview
Gemini 1.5 Pro: A Significant Upgrade
Google has recently introduced Gemini 1.5 Pro, a powerful AI model designed to replace its predecessor with enhanced capabilities. This multimodal model can process an impressive ~700,000 words or ~30,000 lines of code, which is 35 times more than Gemini 1.0 Pro. Additionally, it can analyze up to 11 hours of audio or an hour of video in various languages, although the processing time for searching a specific scene in a one-hour video can take 30 seconds to a minute.
Gemini 1.5 Pro entered public preview on Vertex AI in April. Another endpoint, Gemini Pro Vision, can process both text and imagery, including photos and videos, and generate text output similar to OpenAI’s GPT-4 with Vision model.
Using Gemini Pro in Vertex AI. Image Credits: Gemini
Within Vertex AI, developers can customize Gemini Pro for specific contexts and use cases through fine-tuning or “grounding.” The model can also be connected to external, third-party APIs to perform particular actions. AI Studio offers workflows for creating structured chat prompts using Gemini Pro, with access to both Gemini Pro and Gemini Pro Vision endpoints. Developers can adjust the model temperature to control the output’s creative range, provide examples for tone and style instructions, and tune the safety settings.
Gemini Nano: Efficient On-Device AI
Gemini Nano, a smaller version of the Gemini Pro and Ultra models, is efficient enough to run directly on some phones without relying on a server. It currently powers features like Summarize in Recorder and Smart Reply in Gboard on the Pixel 8 Pro, Pixel 8, and Samsung Galaxy S24.
The Recorder app allows users to record and transcribe audio, and Gemini provides summaries of the recorded content without requiring a signal or Wi-Fi connection. Gboard’s Smart Reply feature, powered by Gemini Nano, suggests the next thing to say when having a conversation in a messaging app, initially working with WhatsApp and expanding to more apps over time.
In the Google Messages app on supported devices, Nano enables Magic Compose, which can craft messages in various styles like “excited,” “formal,” and “lyrical.”
Comparing Gemini to OpenAI’s GPT-4
Google has repeatedly emphasized Gemini’s superiority on benchmarks, claiming that Gemini Ultra surpasses current state-of-the-art results on most widely used academic benchmarks. However, the scores appear to be only marginally better than OpenAI’s corresponding models. Early impressions of the older version of Gemini Pro have highlighted issues with basic facts, translations, and coding suggestions.
Pricing and Availability
Gemini 1.5 Pro is currently free to use in the Gemini apps, AI Studio, and Vertex AI. Once it exits preview in Vertex, the model will cost $0.0025 per character for input and $0.00005 per character for output. Customers pay per 1,000 characters (approximately 140 to 250 words) and per image ($0.0025) for models like Gemini Pro Vision. Summarizing a 500-word article with Gemini 1.5 Pro would cost $5, while generating a similar-length article would cost $0.1. Ultra pricing has not been announced yet.
Gemini Pro and Ultra can be experienced in the Gemini apps, Vertex AI via an API, and AI Studio. Developers can iterate prompts and Gemini-based chatbots in AI Studio and export the code to a more fully featured IDE. Code Assist, Google’s suite of AI-powered assistance tools for code completion and generation, also utilizes Gemini models, allowing developers to perform large-scale changes across codebases.
Google has also integrated Gemini models into its dev tools for Chrome, Firebase mobile dev platform, and databases, showcasing the company’s commitment to expanding the capabilities of its AI offerings.
Google Infuses Generative AI into Cloud Offerings and Security Solutions
Google has made significant strides in integrating generative AI capabilities into its cloud services and security products. The tech giant recently unveiled a suite of AI-powered tools designed to enhance database creation and management, streamlining the process for developers and businesses alike.
In addition to bolstering its cloud offerings, Google has also introduced innovative security solutions that leverage the power of its Gemini AI system. One notable example is Gemini in Threat Intelligence, a key component of Google’s Mandiant cybersecurity platform. This cutting-edge tool harnesses the capabilities of generative AI to analyze vast amounts of potentially malicious code, enabling users to conduct natural language searches for ongoing threats or indicators of compromise.
By incorporating generative AI into its security arsenal, Google aims to provide organizations with advanced threat detection and mitigation capabilities. The integration of Gemini in Threat Intelligence within the Mandiant platform empowers security teams to proactively identify and respond to emerging threats, strengthening their overall cybersecurity posture.
As Google continues to push the boundaries of AI-driven innovation, businesses can expect to benefit from enhanced cloud services and more robust security measures. The fusion of generative AI with Google’s existing offerings marks a significant step forward in the company’s mission to deliver cutting-edge technology solutions to its customers.
5 Comments
Google Gemini? Let’s see if it’s more than just fancy tech talk this time.
Gemini by Google, eh? Hold onto your hats, folks, this might just redefine AI as we know it!
Oh, so Google’s deciding to leapfrog into the future of AI now? Interesting move!
Google Gemini stepping in – guess it’s time to buckle up for an AI revolution!
Looks like Google’s about to switch up the game with Gemini, wonder how this will shake things up!