Google Adds AI Mode in Search, Uses Gemini 2.0 to Answer &#8216;Harder&#8217; Queries
Google on Wednesday announced a new ‘AI Mode’ for search as a part of their experiment in labs. This feature is an upgrade to the AI Overviews feature, capable of ‘advanced reasoning’, and multimodal capabilities. As per the demonstration video, AI mode will exist as a standalone panel in the Google Search interface. The feature uses a ‘custom version’ of Gemini 2.0, which Google says is particularly helpful for questions that need further exploration, comparisons and reasoning. “You can not only access high-quality web content, but also tap into fresh, real-time sources like the Knowledge Graph, info about the real world, and shopping data for billions of products,” said Google in the announcement. AI Mode is said to use a ‘query fan-out’ technique, and issue multiple related searches concurrently across multiple subtopics and data sources, and then compile the results. The feature is also capable of citing real-time information. “So if you ask, ‘what’s the difference in sleep tracking features between a smart ring, smartwatch and tracking mat,’ the custom version of Gemini 2.0 uses a multistep approach to make a plan, conduct searches to find information and adjust the plan based on what it finds,” added Google. The feature can be tested in Labs by users with Google One AI Premium subscription. Further, Google also said that they are rolling out AI Overviews for more users, and one doesn’t need to sign in to access the feature. Google’s new AI mode will compete with the AI-enabled search app Perplexity, its deep research feature, and ChatGPT search, which was recently made free to all users. On Monday, Google released a Data Science Agent on the Colab platform, powered by its Gemini 2.0 AI model. The agent achieves goals set by the user by “orchestrating a composite flow” which mimics the workflow of a typical data scientist. Users can use the agent to clean data, perform exploratory data analysis, statistical analysis, predictive modeling, and perform other tasks.
It uses a ‘query fan-out’ technique, issues multiple related searches concurrently across multiple subtopics and data sources, and then compiles a result.
It uses a ‘query fan-out’ technique, issues multiple related searches concurrently across multiple subtopics and data sources, and then compiles a result.
["AI (Artificial Intelligence)", "Google", "Google Search"]
["data science", "ChatGPT", "Gemini 2.0", "Go", "AI", "Google Search", "Modal", "Colab", "GPT", "Google", "R", "AI (Artificial Intelligence)", "llm_models:GPT"]
["AI", "data science", "ChatGPT", "Gemini 2.0", "Colab", "R", "Go", "GPT", "Modal", "llm_models:GPT"]
Supreeth is an engineering graduate who is curious about the world of artificial intelligence and loves to write stories on how it is solving problems and shaping the future of humanity.