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A Comprehensive Guide To Google Gemini

Gemini is Google’s generative AI model, app, and service flagship, and it’s aiming to cause a stir. Our initial evaluation of Gemini found that, although it shows promise in certain areas, it fails to deliver in others.

Now the common questions are What exactly is Gemini? What does it do? How does it compare to similar products? Don’t worry we have compiled this helpful guide to keep you informed of all the latest Gemini news and advancements to date.

What Exactly Is Gemini?

Developed by Google’s artificial intelligence research laboratories DeepMind and Google Research, Gemini is the next-generation GenAI model family that Google has long promised.

There are three varieties to choose from.

  • Gemini Ultra, the flagship Gemini model.
  • Gemini Pro, is a “lite” version of the original Gemini.
  • Gemini Nano, the smaller “distilled” variant, that is compatible with mobile devices such as the Pixel 8 Pro.

The training of all Gemini models ensures that they are “natively multimodal” that is, capable of processing and using data in contexts beyond text alone. They were trained and fine-tuned using a wide range of media types, including audio, photos, videos, codebases, and text in several languages.

The fact that Gemini isn’t trained on text alone distinguishes it from models like Google’s own LaMDA. Contrary to what you may expect from LaMDA, Gemini models can comprehend and produce non-textual content such as essays and email drafts.

How Does The Gemini Models And Apps Differ?

Once again demonstrating its branding shortcomings, Google failed to clearly distinguish Gemini from its online and mobile applications (previously Bard) from the very beginning. Like a client for Google’s GenAI, the Gemini applications are only a means by which users may access certain Gemini models.

Gemini applications and models are completely separate from Imagen 2, Google’s text-to-image model that is integrated into several of the company’s development environments and tools. Rest assured, you are not alone in being perplexed by this.

How Does Gemini Function?

As a result of their multimodal nature, the Gemini models have the potential to generate artwork, caption photos, and videos, and transcribe spoken words. Even while only a handful of these features are now available to the public, Google has promised to release them all (and more) in the near future. It isn’t exactly easy to believe the firm when it says it will do anything.

When they first released Bard, Google failed to live up to expectations. More recently, it caused a stir with a video that seemed to showcase Gemini’s skills but was later revealed to be excessively edited and rather idealistic.

Nonetheless, these are the capabilities that the various Gemini levels will have when they reach their maximum potential if Google is being somewhat true to its claims.

Gemini Ultra:

Google claims that Gemini Ultra’s multimodality makes it useful for a variety of tasks, including assisting with physics homework, providing step-by-step solutions on a worksheet, and identifying potential errors in previously submitted answers.

Google states that Gemini Ultra may be used for tasks including finding relevant scientific publications, retrieving information from those papers, and “updating” a chart by producing the formulae needed to re-create the graphic with more recent data.

As said before, Gemini Ultra can theoretically generate images. However, this feature is still missing from the productized model. This might be because the process is more involved than how image-generating applications like ChatGPT work. Instead of passing commands to an image generator (such as DALL-E 3 in the case of ChatGPT), Gemini produces pictures “natively,” bypassing the middleman.

As an application programming interface (API), Gemini Ultra is accessible through AI Studio, Google’s web-based service for app and platform developers, and Vertex AI, Google’s completely managed AI developer platform. The Gemini applications also run on it, but you’ll have to pay for it. Subscribing to the $20/month Google One AI Premium Plan grants access to Gemini Ultra via what Google refers to as Gemini Advanced.

If you have the AI Premium Plan, Gemini may be integrated with your whole Google Workspace account. This includes Gmail, Docs, Sheets, and Google Meet recordings, among other things. For example, you may use it to have Gemini take notes during a video conversation or to summarize emails.

Gemini Pro:

According to Google, Gemini Pro’s thinking, planning, and comprehension capabilities are superior to LaMDA’s.

When it comes to processing reasoning chains that are longer and more complicated, Gemini Pro does a better job than OpenAI’s GPT-3.5, according to an independent analysis conducted by researchers from Carnegie Mellon and BerriAI. However, the study also discovered that Gemini Pro, similar to other big language models, had a hard time with multi-digit math issues and that users have discovered several instances of faulty reasoning and errors. However, Google has delivered on its promises of enhancements, with Gemini 1.5 Pro being the first of its kind.

The new and enhanced Gemini 1.5 Pro, which is now in preview, is a drop-in replacement that offers several improvements over its predecessor. One of the most notable is its ability to analyze large amounts of data. About 30,000 lines of code or 700,000 words can be handled by Gemini 1.5 Pro in restricted private preview, which is 35 times more than what Gemini 1.0 Pro could do. Plus, it’s not only restricted to text because the model is multimodal. Even though it’s sluggish, Gemini 1.5 Pro can analyze as much as eleven hours of audio or one hour of video in several languages. For example, finding a scene in an hour-long film takes thirty seconds to a minute of processing.

You can also access Gemini Pro using the Vertex AI API; it takes text as input and outputs text. Like OpenAI’s GPT-4 with Vision model, Gemini Pro Vision is an extra endpoint that can handle text and images (pictures and videos) and produce text.

Gemini Pro may be fine-tuned or “grounding” to suit certain situations and use cases by developers within Vertex AI. Additionally, third-party APIs can be integrated with Gemini Pro to carry out certain tasks.

Workflows for developing Gemini Pro-based structured chat prompts are available in AI Studio. Developers may access both the Gemini Pro and Gemini Pro Vision endpoints; they can tweak the safety settings, alter the model temperature to regulate the output’s creative range, and provide examples to advise on tone and style.

Gemini Nano:

A more compact alternative to the larger Gemini Pro and Ultra variants, the power-efficient Gemini Nano may handle tasks locally on (certain) phones rather than transferring them to a remote server. Currently, it is responsible for two functions for the Pixel 8 Pro: Conclude using Recorder and Gboard’s Smart Reply.

Use the Recorder app to easily record and transcribe audio at the touch of a button. It comes with a Gemini-powered summary that you can use for interviews, clips from presentations, and more. For users’ peace of mind, no data is sent from their phone during this procedure, and they may see these summaries regardless of whether they have a signal or Wi-Fi connection.

You may also find Gemini Nano in the development preview of Google’s keyboard software, Gboard. There, it drives Smart Reply, a feature that suggests what you should say next in your messaging conversation. Initially, the function is only compatible with WhatsApp, but according to Google, it will be available in additional applications in 2024.

How does Gemini stack up against OpenAI’s GPT-4?

According to Google, Gemini Ultra outperforms current state-of-the-art results on “30 of the 32 widely used academic benchmarks used in large language model research and development.” This is one of numerous instances where Google boasts about Gemini’s benchmark supremacy. Meanwhile, the business claims that Gemini Pro outperforms GPT-3.5 in areas such as content summarization, ideation, and writing.

Apart from the issue of whether benchmarks truly show a superior model, Google’s results seem to be slightly higher than OpenAI’s similar models. Plus, as said before, not all first impressions have been positive. Users and scholars have pointed out how Gemini Pro has a tendency to misinterpret simple facts, has trouble with translations, and provides bad code advice.

When will Gemini be available?

Currently, you may utilize Gemini Pro in the Gemini applications, AI Studio, and Vertex AI for free. Unfortunately, when Gemini Pro leaves Vertex’s preview, the model will cost $0.0025/character, and the output will cost $0.00005/character. Customers of Vertex pay 0.0025 per picture and a flat rate per 1,000 characters (140–250 words) for models such as Gemini Pro Vision.

So, we’ll pretend there are 2,000 characters in a 500-word piece. Using Gemini Pro to summarize the article would set you back $5. However, the cost to generate an article of the same length would be $0.1. The price of the ultra has not been revealed just yet.

 Where You Can Use Gemini?

Gemini Pro:

You can get the most out of Gemini Pro on the Gemini mobile app. The Pro and Ultra versions are responding to questions in several languages.

Vertex AI also provides preview access to Gemini Pro and Ultra through an API. At this point, the API is available for free “within limits” and serves specific locations, such as Europe. It also has capabilities like filtering and chat functionality.

Additionally, AI Studio is where you may find Gemini Pro and Ultra. Developers may receive API keys to employ Gemini-based chatbots and iterative prompts in their apps through the service, or they can export the source code to a more feature-rich integrated development environment.

The Google AI-powered code completion and generation package, Duet AI for Developers, has switched to using Gemini models. Additionally, Google has integrated Gemini models into its Chrome and Firebase mobile app development tools.

Gemini Nano:

The Pixel 8 Pro has Gemini Nano, and other devices will get it later on. Signing up for a sneak look is an option for developers who are interested in using the model in their Android apps.

Editorial Staff
Editorial Staff
Editorial Staff at AI Surge is a dedicated team of experts led by Paul Robins, boasting a combined experience of over 7 years in Computer Science, AI, emerging technologies, and online publishing. Our commitment is to bring you authoritative insights into the forefront of artificial intelligence.


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