Google Gemini and SEO, what's changing?
Posted: Wed Dec 04, 2024 10:11 am
The race in the field of AI continues to intensify between Google and OpenAI. OpenAI released GPT-4 a few months ago, while Google introduced its “multimodal system” at the May 2023 Google IO conference: Gemini. The term, often associated with the Gemini constellation or the second space flight just before Apollo, stands for “Generalized Multimodal Intelligence Network” in Google’s project.
What we know about Gemini
Google has reportedly given several companies access to an early version of its Gemini system. Here's a look at what's being filtered about this "multimodal system."
"Imagine if the Hulk of language models and Tony Stark's AI Jarvis had a child... Boom! This is Gemini." Online, tech enthusiasts are singing the praises of Google's generative AI system, with plenty of happy pop culture references.
So how does the Gemini multimodal model work? What are its features? Does it deserve all the praise it has received even before its launch?
The previous ChatGPT tends to convince us that nuance would be more appropriate: if OpenAI’s generative model had surpassed 100 million users by January 2023, its engagement stagnated in May and began to decline in June. What’s more armenia phone number resources the OpenAI model is not without risks and has even shown some signs of decline.
Gemini is designed to be "multimodal and extremely efficient in integrating tools and APIs," according to the Mountain View company , and is expected to "enable future innovations like memory and scheduling."
Gemini Development
To train this massive model, Gemini relies on the breadth and depth of data accumulated by Alphabet, particularly through platforms like YouTube, Google Books, Google Search, and Google Scholar. It also uses cutting-edge training chips called TPUv5, which are claimed to be the only chip in the world that can run 16,384 chips simultaneously. Google’s teams also trained the model using methods similar to those used to develop AlphaGo, a game more complex than chess. And unlike Google’s large spoken language model LaMDA, which was 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 , including Google co-founder Sergey Brin, are currently working on the project. The same source also said that Google could introduce Gemini as an update to Google Bard or as a new chatbot before using it to power various products like Google Docs. Gemini could be released soon, likely in response to OpenAI’s GPT-4.5 release ahead of GPT-5, which is expected in early 2024. “Gemini will be available in different sizes and capacities, such as PaLM 2, once it has been refined and rigorously tested for security,” Google said, without providing further details.
A Potentially Shortened User Journey
Currently, Google SGE (Google’s AI-enhanced search experience) is being tested in about a hundred countries. This version of Google offers AI-generated text, resources, and a speech module. For certain queries, this search engine can reduce the number of user queries. According to an example from Exposure Ninja, a user looking for information about “real estate attorney” for a move may visit only four sites instead of eight with a traditional search.
User Search by Exposure Ninja
Source: Source exposure ninja
What happens if Gemini eventually integrates with SGE? "The costs associated with rolling out Gemini's responses to SGE mean that Google is unlikely to offer Gemini-based SGE results unless necessary," cautions Tim Cameron-Kitchen, founder of Exposure Ninja.
In the case of Gemini in SGE, the multimodal system’s ability to anticipate users’ presumed needs can further reduce the search phase. Using Gemini can provide direct answers to the user’s next questions in the search results. According to Exposure Ninja, in the previous example, this could create a search journey with only three sites to visit.
User Search Intention
Source: Source exposure ninja
According to Tim Cameron-Kitchen, using Gemini in this way at SGE could also lead to “less duplication, better structured answers that logically follow the searcher’s path, and better integration of multimodal capabilities.” According to this digital marketing expert, the potential decrease in site visits could be offset by the fact that links are still present in the answers generated and people continue to shop from sites through Google.
Potential applications of Gemini
Gemini has the potential to be used in a variety of applications, including:
Chatbots: Gemini can be used to create more sophisticated and natural chatbots. Gemini-based chatbots can be used to provide customer service, answer questions, or even just chat.
Text summaries: Gemini can be used to create more accurate and concise text summaries. Gemini-based text summaries can be used to help people understand long articles or documents.
Creative Creators: Gemini can be used to produce creative content such as poetry, screenplays, or music. Gemini-based creative creators can be used to create new forms of art or entertainment.
Machine learning applications: Gemini can be used to improve the performance of machine learning applications. Gemini can be used to train more accurate and powerful machine learning models.
How we can use Google Gemini AI
During Google I/O 2023, Google Alphabet CEO Sundar Pichai highlighted the progress being made in making generative AI more user-friendly. These developments include PaLM 2 and Gemini. DeepMind’s Gemini is specifically crafted to be multi-modal, allowing it to understand a variety of data types, including text, images, and code. This versatility makes it perfect for a variety of tasks:
Creating various types of text, translating languages, and creating a variety of creative content.
Manipulating data formats such as graphs and maps.
Leveraging a large knowledge base 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 unfamiliar questions.
Gemini’s multimodal processing capabilities are still in their infancy, but they have the potential to revolutionize human-computer interactions. Applications could range from creating more realistic and engaging virtual assistants to innovating educational tools to better understanding the world. Read on for more details about Google’s Gemini AI, including how it works, its key features, and more.
What we know about Gemini
Google has reportedly given several companies access to an early version of its Gemini system. Here's a look at what's being filtered about this "multimodal system."
"Imagine if the Hulk of language models and Tony Stark's AI Jarvis had a child... Boom! This is Gemini." Online, tech enthusiasts are singing the praises of Google's generative AI system, with plenty of happy pop culture references.
So how does the Gemini multimodal model work? What are its features? Does it deserve all the praise it has received even before its launch?
The previous ChatGPT tends to convince us that nuance would be more appropriate: if OpenAI’s generative model had surpassed 100 million users by January 2023, its engagement stagnated in May and began to decline in June. What’s more armenia phone number resources the OpenAI model is not without risks and has even shown some signs of decline.
Gemini is designed to be "multimodal and extremely efficient in integrating tools and APIs," according to the Mountain View company , and is expected to "enable future innovations like memory and scheduling."
Gemini Development
To train this massive model, Gemini relies on the breadth and depth of data accumulated by Alphabet, particularly through platforms like YouTube, Google Books, Google Search, and Google Scholar. It also uses cutting-edge training chips called TPUv5, which are claimed to be the only chip in the world that can run 16,384 chips simultaneously. Google’s teams also trained the model using methods similar to those used to develop AlphaGo, a game more complex than chess. And unlike Google’s large spoken language model LaMDA, which was 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 , including Google co-founder Sergey Brin, are currently working on the project. The same source also said that Google could introduce Gemini as an update to Google Bard or as a new chatbot before using it to power various products like Google Docs. Gemini could be released soon, likely in response to OpenAI’s GPT-4.5 release ahead of GPT-5, which is expected in early 2024. “Gemini will be available in different sizes and capacities, such as PaLM 2, once it has been refined and rigorously tested for security,” Google said, without providing further details.
A Potentially Shortened User Journey
Currently, Google SGE (Google’s AI-enhanced search experience) is being tested in about a hundred countries. This version of Google offers AI-generated text, resources, and a speech module. For certain queries, this search engine can reduce the number of user queries. According to an example from Exposure Ninja, a user looking for information about “real estate attorney” for a move may visit only four sites instead of eight with a traditional search.
User Search by Exposure Ninja
Source: Source exposure ninja
What happens if Gemini eventually integrates with SGE? "The costs associated with rolling out Gemini's responses to SGE mean that Google is unlikely to offer Gemini-based SGE results unless necessary," cautions Tim Cameron-Kitchen, founder of Exposure Ninja.
In the case of Gemini in SGE, the multimodal system’s ability to anticipate users’ presumed needs can further reduce the search phase. Using Gemini can provide direct answers to the user’s next questions in the search results. According to Exposure Ninja, in the previous example, this could create a search journey with only three sites to visit.
User Search Intention
Source: Source exposure ninja
According to Tim Cameron-Kitchen, using Gemini in this way at SGE could also lead to “less duplication, better structured answers that logically follow the searcher’s path, and better integration of multimodal capabilities.” According to this digital marketing expert, the potential decrease in site visits could be offset by the fact that links are still present in the answers generated and people continue to shop from sites through Google.
Potential applications of Gemini
Gemini has the potential to be used in a variety of applications, including:
Chatbots: Gemini can be used to create more sophisticated and natural chatbots. Gemini-based chatbots can be used to provide customer service, answer questions, or even just chat.
Text summaries: Gemini can be used to create more accurate and concise text summaries. Gemini-based text summaries can be used to help people understand long articles or documents.
Creative Creators: Gemini can be used to produce creative content such as poetry, screenplays, or music. Gemini-based creative creators can be used to create new forms of art or entertainment.
Machine learning applications: Gemini can be used to improve the performance of machine learning applications. Gemini can be used to train more accurate and powerful machine learning models.
How we can use Google Gemini AI
During Google I/O 2023, Google Alphabet CEO Sundar Pichai highlighted the progress being made in making generative AI more user-friendly. These developments include PaLM 2 and Gemini. DeepMind’s Gemini is specifically crafted to be multi-modal, allowing it to understand a variety of data types, including text, images, and code. This versatility makes it perfect for a variety of tasks:
Creating various types of text, translating languages, and creating a variety of creative content.
Manipulating data formats such as graphs and maps.
Leveraging a large knowledge base 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 unfamiliar questions.
Gemini’s multimodal processing capabilities are still in their infancy, but they have the potential to revolutionize human-computer interactions. Applications could range from creating more realistic and engaging virtual assistants to innovating educational tools to better understanding the world. Read on for more details about Google’s Gemini AI, including how it works, its key features, and more.