AI

Google’s Astra is its first AI-for-everything agent

1 Mins read

Google is unveiling many more new AI capabilities beyond agents today. It’s going to integrate AI more deeply into Search through a new feature called AI overviews, which gather information from the internet and package them into short summaries in response to search queries. The feature, which launches today, will initially be available only in the US, with more countries to gain access later. 

This will help speed up the search process and get users more specific answers to more complex, niche questions, says Felix Simon, a research fellow in AI and digital news at the Reuters Institute for Journalism. “I think that’s where Search has always struggled,” he says. 

Another new feature of Google’s AI Search offering is better planning. People will soon be able to ask Search to make meal and travel suggestions, for example, much like asking a travel agent to suggest restaurants and hotels. Gemini will be able to help them plan what they need to do or buy to cook recipes, and they will also be able to have conversations with the AI system, asking it to do anything from relatively mundane tasks, such as informing them about the weather forecast, to highly complex ones like helping them prepare for a job interview or an important speech. 

People will also be able to interrupt Gemini midsentence and ask clarifying questions, much as in a real conversation. 

In another move to one-up competitor OpenAI, Google also unveiled Veo, a new video-generating AI system. Veo is able to generate short videos and allows users more control over cinematic styles by understanding prompts like “time lapse” or “aerial shots of a landscape.”

Google has a significant advantage when it comes to training generative video models, because it owns YouTube. It’s already announced collaborations with artists such as Donald Glover and Wycleaf Jean, who are using its technology to produce their work. 


Source link

Related posts
AI

PRISE: A Unique Machine Learning Method for Learning Multitask Temporal Action Abstractions Using Natural Language Processing (NLP)

2 Mins read
In the domain of sequential decision-making, especially in robotics, agents often deal with continuous action spaces and high-dimensional observations. These difficulties result…
AI

FLUTE: A CUDA Kernel Designed for Fused Quantized Matrix Multiplications to Accelerate LLM Inference

3 Mins read
Large Language Models (LLMs) face deployment challenges due to latency issues caused by memory bandwidth constraints. Researchers use weight-only quantization to address…
AI

Self-Route: A Simple Yet Effective AI Method that Routes Queries to RAG or Long Context LC based on Model Self-Reflection

3 Mins read
Large Language Models (LLMs) have revolutionized the field of natural language processing, allowing machines to understand and generate human language. These models,…

 

 

Leave a Reply

Your email address will not be published. Required fields are marked *