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Google’s AI Co-Scientist vs. OpenAI’s Deep Research vs. Perplexity’s Deep Research: A Comparison of AI Research Agents

Unite.AI

Rapid advancements in AI have brought about the emergence of AI research agentstools designed to assist researchers by handling vast amounts of data, automating repetitive tasks, and even generating novel ideas. As Perplexity's Deep Research focuses on knowledge discovery, it has a limited scope as a research agent.

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The Emergence of Self-Reflection in AI: How Large Language Models Are Using Personal Insights to Evolve

Unite.AI

As AI moves closer to Artificial General Intelligence (AGI) , the current reliance on human feedback is proving to be both resource-intensive and inefficient. This shift represents a fundamental transformation in AI learning, making self-reflection a crucial step toward more adaptable and intelligent systems.

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Gemini 2.0: Google ushers in the agentic AI era 

AI News

model, this major upgrade incorporates enhanced multimodal capabilities, agentic functionality, and innovative user tools designed to push boundaries in AI-driven technology. Leap towards transformational AI Reflecting on Googles 26-year mission to organise and make the worlds information accessible, Pichai remarked, If Gemini 1.0

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Hugging Face calls for open-source focus in the AI Action Plan

AI News

million public models across various sectors and serves seven million users, proposes an AI Action Plan centred on three interconnected pillars: Hugging Face stresses the importance of strengthening open-source AI ecosystems. The company prioritises efficient and reliable adoption of AI. Hugging Face, which hosts over 1.5

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Magic Behind Anthropic’s Contextual RAG for AI Retrieval

Analytics Vidhya

In an era where artificial intelligence (AI) is tasked with navigating and synthesizing vast amounts of information, the efficiency and accuracy of retrieval methods are paramount. Anthropic, a leading AI research company, has introduced a groundbreaking approach called Contextual Retrieval-Augmented Generation (RAG).

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Meta AI Researchers Introduced SWEET-RL and CollaborativeAgentBench: A Step-Wise Reinforcement Learning Framework to Train Multi-Turn Language Agents for Realistic Human-AI Collaboration Tasks

Marktechpost

FAIR at Meta and UC Berkeley researchers proposed a new reinforcement learning method called SWEET-RL (Step-WisE Evaluation from Training-time Information). The critic has access to additional information during training, such as the correct solution, which is not visible to the actor. and frontend win rates from 38.6%

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New AI training techniques aim to overcome current challenges

AI News

Reportedly led by a dozen AI researchers, scientists, and investors, the new training techniques, which underpin OpenAI’s recent ‘o1’ model (formerly Q* and Strawberry), have the potential to transform the landscape of AI development. The include xAI , Google DeepMind , and Anthropic.