Google Gemini: Everything it’s essential know concerning the latest generative AI platform

Google is attempting to make a splash with Gemini, a flagship suite of generative AI models, apps and services. But while Gemini appears to be promising in some elements, it falls short in others – as our informal review revealed.

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

To make it easier to stay awake thus far with the most recent 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 Ultrathe flagship 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’re able to work with and use greater than just words. They have been pre-trained and refined using quite a lot of audio, image and video files, a considerable amount of code bases and texts in several languages.

This sets Gemini other than models like Google's own LaMDA, which was trained solely on text data. LaMDA cannot understand or generate anything aside 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 may be accessed – consider it as a client for Google's GenAI.

By the best way, the Gemini apps and models are also completely independent of Imagen 2, Google's text-to-image model, which is offered in a few of the company's development tools and environments. Don't worry – you're not the just one confused by this.

What can Gemini do?

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

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

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

Assuming that Google's claims are kind of true, here's what different Gemini stages can do once they reach their full potential:

Gemini Ultra

Google says Gemini Ultra's multimodality means it will 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 keeping with Google, may also be applied to tasks reminiscent of 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 best way 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 offered 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 – but not free of charge. Accessing Gemini Ultra through what Google calls Gemini Advanced requires a subscription to the Google One AI Premium plan, priced at $20 per 30 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 beneficial, 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 Gemini Pro is definitely higher than OpenAI's GPT-3.5 at coping with longer and more complex reasoning chains. But the study also found that, like all major language models, Gemini Pro struggles primarily with multi-digit math problems, and users have found many examples of poor pondering and errors.

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

Gemini 1.5 Pro (currently in preview) was designed as a alternative and has some improvements in comparison with its predecessor, but most significantly the quantity of knowledge it will possibly process. Gemini 1.5 Pro can hold (in limited private preview) about 700,000 words or about 30,000 lines of code – 35 times the quantity that Gemini 1.0 Pro can handle. And because the 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. processing a scene in an hour-long video takes 30 seconds to a minute).

Gemini Pro can also be available through the API in Vertex AI to just accept text as input and generate text as output. 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 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 likewise tweak security settings.

However, once Gemini Pro leaves preview in Vertex, the model costs $0.0025 per character, while the output costs $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 accommodates 2,000 characters. Summary of this text using Gemini Pro would cost $5. Meanwhile, creating an article of comparable length would cost $0.1.

Ultra pricing has yet to be announced.