Google gets "reasoning" AI in major Google update - Technology News
Google has rolled out a significant update to its Gemini 2.0 model family, introducing powerful new features that focus on enhanced reasoning abilities and expanded functionalities for developers and advanced users. The new updates aim to make AI smarter, more efficient, and capable of handling complex tasks with improved accuracy.
Improved Performance with Gemini 2.0 Flash Model
Google's 2.0 Flash model is now widely accessible via the Gemini API in Google AI Studio and Vertex AI. This powerful, high-performance model is optimised for low-latency operations, making it ideal for developers working on high-frequency, large-scale applications. It boasts a context window of 1 million tokens, allowing it to process vast amounts of information quickly and effectively.
Enhanced Capabilities with Gemini 2.0 Pro
Gemini 2.0 Pro is designed to handle complex prompts and coding tasks, providing developers with a tool that excels in analysing and understanding intricate information. With an expanded context window of 2 million tokens, the model is capable of delivering deeper insights and performing more advanced reasoning tasks. It also integrates seamlessly with Google Search and code execution tools.
Cost-effective Solution with Gemini 2.0 Flash-Lite
In response to feedback from the developer community, Google has introduced the 2.0 Flash-Lite model. This new version offers the same speed and functionality as the original 2.0 Flash, but at a significantly lower cost. Flash-Lite excels in generating captions for large-scale image datasets or completing similar tasks at a fraction of the price.
Advanced Reasoning Capabilities
A standout feature of these new updates is the integration of advanced reasoning capabilities. The new models can not only process and understand text inputs but also analyse complex multimodal inputs, such as images and audio, enabling more comprehensive tasks and better decision-making. Google has incorporated reinforcement learning techniques, allowing the models to evaluate and critique their responses for improved reasoning.
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