Google Gemini: Everything you must know concerning the latest generative AI platform

Google is attempting to make a splash with Gemini, its flagship suite of generative AI models, apps and services.

So what are twins? How can you utilize it? And how does it compare to the competition?

To make it easier to not sleep up to now with the newest Gemini developments, we've put together this handy guide. We'll keep it updated as latest Gemini models, features, and news about Google's plans for Gemini are released.

What are twins?

Gemini belongs to Google promised for a very long time, next-generation GenAI model family developed by Google's AI research labs DeepMind and Google Research. It is available in three flavors:

• Gemini Ultraprobably the most powerful Gemini model.
• Gemini Proa “lightweight” Gemini model.
• Gemini Nanoa smaller “distilled” model that runs on mobile devices just like the Pixel 8 Pro.

All Gemini models have been trained to be “natively multimodal” – in other words, they can work with and use greater than just words. They have been pre-trained and refined using a wide range of audio, image and video files, a considerable amount of code bases and texts in numerous languages.

This sets Gemini aside from models like Google's own LaMDA, which was trained solely on text data. LaMDA cannot understand or generate anything apart from text (e.g. essays, email drafts), but that will not be the case with Gemini models.

What is the difference between the Gemini apps and the Gemini models?

Google has once more proven that it lacks a way of branding and has not made it clear from the beginning that Gemini is separate and distinct from the Gemini web and mobile apps (formerly Bard). The Gemini apps are simply an interface through which specific Gemini models might be accessed – consider it as a client for Google's GenAI.

By the way in which, the Gemini apps and models are also completely independent of Imagen 2, Google's text-to-image model, which is out there in a number of the company's development tools and environments.

What can Gemini do?

Because the Gemini models are multimodal, they’ll theoretically perform a variety of multimodal tasks, from transcribing speech to subtitling images and videos to creating artwork. Some of those features have already reached product stage (more on that later), and Google guarantees all of them – and more – in some unspecified time in the future within the not-too-distant future.

Of course, it's just a little difficult to take the corporate at its word.

Google significantly under-delivered when it originally launched Bard. And recently a video caused a stir that was imagined to display Gemini's capabilities, but turned out to be heavily manipulated and roughly ambitious.

Assuming that Google's claims are roughly true, here's what the several Gemini stages can do once they reach their full potential:

Gemini Ultra

Google says Gemini Ultra's multimodality means it may possibly be used for things like physics homework, solving problems step-by-step on a worksheet, and mentioning possible errors in already accomplished answers.

Gemini Ultra, in line with Google, may also be applied to tasks corresponding to identifying scientific papers relevant to a selected problem – extracting information from those papers and “updating” a graph from them by generating the formulas needed to try this Rebuild chart with newer data.

Gemini Ultra technically supports image creation, as already mentioned. However, this feature has not yet made its way into the product version of the model – perhaps since the mechanism is more complex than the way in which apps like ChatGPT generate images. Instead of passing prompts to a picture generator (like DALL-E 3 within the case of ChatGPT), Gemini outputs images “natively,” without an intermediate step.

Gemini Ultra is out there as an API through Vertex AI, Google's fully managed AI developer platform, and AI Studio, Google's web-based tool for app and platform developers. It also runs the Gemini apps – although not without spending a dime. Accessing Gemini Ultra through what Google calls Gemini Advanced requires a subscription to the Google One AI Premium plan, priced at $20 per thirty days.

The AI ​​Premium plan also connects Gemini to your broader Google Workspace account – think emails in Gmail, documents in Docs, presentations in Sheets, and Google Meet recordings. This is helpful, for instance, for summarizing emails or having Gemini take notes during a video call.

Gemini Pro

According to Google, Gemini Pro represents an improvement over LaMDA in its reasoning, planning and comprehension capabilities.

An independent study from Carnegie Mellon and BerriAI researchers found that the primary version of Gemini Pro could actually handle longer and more complex reasoning chains higher than OpenAI's GPT-3.5. However, the study also found that this version of Gemini Pro, like all major language models, particularly struggled with multi-digit math problems, and users found examples of poor considering and obvious errors.

However, Google promised a treatment – and the primary got here in the shape of Gemini 1.5 Pro.

Gemini 1.5 Pro is designed as a alternative and features improvements over its predecessor in several areas, but most significantly in the quantity of information it may possibly process. Gemini 1.5 Pro can hold roughly 700,000 words or roughly 30,000 lines of code – 35 times the quantity that Gemini 1.0 Pro can handle. And for the reason that model is multimodal, it will not be limited to text. Gemini 1.5 Pro can analyze as much as 11 hours of audio or an hour of video in various languages, albeit slowly (e.g., looking for a scene in an hour-long video takes 30 seconds to a minute of processing time).

Gemini 1.5 Pro was introduced in April as a public preview of Vertex AI.

An additional endpoint, Gemini Pro Vision, can process text images – including photos and videos – and output text modeled on OpenAI's GPT-4 with Vision model.

How to make use of Gemini Pro in Vertex AI. Photo credit: Twins

Within Vertex AI, developers can tailor Gemini Pro to specific contexts and use cases using a fine-tuning or “grounding” process. Gemini Pro may also hook up with external third-party APIs to perform certain actions.

There are workflows in AI Studio for creating structured chat prompts with Gemini Pro. Developers have access to the Gemini Pro and Gemini Pro Vision endpoints and might adjust model temperature to manage the creative range of the output, provide examples of tone and magnificence instructions – and in addition tweak security settings.

However, once Gemini 1.5 Pro leaves preview in Vertex, the model will cost $0.0025 per character, while the output will cost $0.00005 per character. Vertex customers pay per 1,000 characters (roughly 140 to 250 words) and, on models like Gemini Pro Vision, per image ($0.0025).

Let's say a 500 word article comprises 2,000 characters. Summary of this text with Gemini 1.5 Pro would cost $5. Meanwhile, creating an article of comparable length would cost $0.1.

Ultra pricing has yet to be announced.