The AI race continues to heat up between Google and Open AI. While the latter launched GPT-4 a few months ago, Google unveiled its “multimodal system” at the Google IO conference in May 2023: Gemini. Typically associated with the albania phone number list Gemini constellation or the second space flight, just before Apollo, in Google’s project, it stands for “Generalized Multimodal Intelligence Network.”
What we know about Gemini
Google has apparently given access to an early version of its Gemini system to some companies.
“Imagine if the Hulk of language models and Jarvis, Tony Stark’s AI, had a baby… Boom!” This is Gemini. ” On the Internet, tech fans are full of praise for Google’s generative artificial intelligence system, with many more or less happy references to pop culture.
But how does the Gemini multimodal model work? What are its specificities? Does it deserve all the superlatives even before its launch?
The Chat GPT above would tend to convince us that nuance would be more appropriate: if Open AI’s generative model surpassed 100 million users in January 2023, its attendance plateaued in May and began to decline in June. Moreover, Open AI’s model is not without risks and has even shown some signs of regression.
According to the Mountain View firm , Gemini was what does an organic positioning agency do designed to be “multimodal, highly efficient in integrating tools and APIs.
Does it deserve all the superlatives even before its launch?
To develop this massive model, Gemini draws notably on the breadth and depth of data Alphabet has accumulated, across platforms like YouTube, Google Books, Google Search, and Google Scholar. It also uses state-of-the-art training chips called TPUv5 — reportedly the only ones in the world capable of orchestrating 16,384 chips working together. Google’s teams also trained the model using methods similar to those used in the singapore data development of AlphaGo, a game more complex than chess. And unlike , Google’s large conversational language model trained using supervised learning, Gemini was trained using reinforcement learning like GPT-3 and GPT-4. This machine learning technique involves an AI agent learning to perform a task through trial and error in a dynamic environment.
According to The Information, several former members of the Google Brain and DeepMind teams are currently working on the project, including Google co-founder Sergey .
A potentially shorter user journey
Additionally, according to the same source, Google could introduce Gemini as an update to Google Bard or the creation of a new chatbot before using Gemini to power various products such as Google Docs. Gemini could be launched soon, possibly in response to upcoming release of GPT-4.5 ahead of GPT-5, which is expected in early 2024. “Once refined and subjected to rigorous security testing, Gemini will be available in different sizes and capacities, much like ,” Google says, without further details.
Google SGE (Google’s AI-enhanced search experience) is currently being tested in around 100 countries. This version of Google offers AI-generated text, fonts, and a conversational module. For certain queries, this search engine could reduce the number of queries users make. According to an example from Exposure Ninja, a user searching for information on a “real estate lawyer” for a moving arrangement might only have four visits to the site instead of eight with a traditional search.
User Search by Exposure Ninja
Source: Source exposure ninja
What happens if Gemini ends up being integrated into SGE? “The costs associated with disseminating Gemini answers on SGE mean that Google is initially reluctant to provide Gemini-based SGE results unless they are needed,” warns Tim Cameron-Kitchen, founder of Exposure Ninja.</p>
In the case of Gemini’s deployment at SGE, the multimodal system’s ability to anticipate users’ presumed needs could further shorten the search ph
ase. Using Gemini could provide direct answers in search results to the user’s next questions. In the example above, this could create a search journey with only three sites to visit, according to Exposure Ninja.
User Search Intention
Source: Source exposure ninja
This use of Gemini in SGE could also bring, according to Tim Cameron-Kitchen, “less duplicates, better structured answers that logically follow the searcher’s path and better integration of multimodal capabilities.
How can we use Google Gemini AI?
DeepMind’s Gemini is specifically designed to be multimodal, allowing it to understand a variety of data types, including text, images, and code. This versatility allows it to excel at a number of tasks:
Generate various types of text, translate languages and create diverse creative content.
Process data formats such as graphs and maps.
Leverage a broad knowledge base derived from extensive training on text and code datasets.
Facilitate the creation of new products and services.
Analyze data and recognize patterns.
Provide informative answers to complex or unconventional questions.
While Gemini’s multimodal processing capability is still in development, it has the potential to revolutionize human-computer interactions. Its applications could range from creating more realistic and engaging virtual assistants to innovating educational tools and improving our understanding of the world. For more details on Google’s Gemini AI, including how it works, notable features, and more, keep exploring.
How does Gemini work?
Gemini operates as a multimodal AI system, capable of processing various types of data, including text, images, and code. It leverages its extensive training on a massive dataset of text and code, allowing it to understand and generate these different forms of information.
At its core, Gemini employs advanced algorithms and models developed by DeepMind to understand and interpret data in multiple formats. By training on diverse datasets, Gemini learns patterns, structures, and relationships within the data, allowing it to perform tasks such as generating text, processing visual information such as charts and maps, and analyzing complex datasets.
Its multimodal capabilities allow Gemini to handle different types of information simultaneously, facilitating tasks that involve multiple formats or data sources. This versatility is what positions Gemini as a potentially transformative tool, capable of revolutionizing the way we interact with computers and process information in various fields.
Training and connection
For SEO professionals, it will likely be essential to fully exploit Gemini’s potential. It will become an essential tool for every SEO,” suggests Giulio Stella, SEO consultant at . “We will need training to use it cautiously and improve our results. Developers would have to pay to access Gemini by renting Google Cloud servers.