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BREAKING 🚨: Google is working on multi-agent systems to help you refine ideas with tournament-like evaluation. Each run takes around 40 minutes and brings you 100 detailed ideas on a given research topic. 2 new multi-agents are being developed for Gemini Enterprise: - Idea Generation - "Create a multi-agent...

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🚨 AI News | TestingCatalog

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569,702 次观看 • 7 个月前 •via X (Twitter)

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2️⃣ Hypothesis Generation Built with Co-Scientist, this system can help brainstorm and evaluate novel research ideas for open challenges. Using a multi-agent “idea tournament”, the system generates, debates and evaluates hypotheses to show what works, what doesn't, and why.
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2️⃣ Hypothesis Generation Built with Co-Scientist, this system can help brainstorm and evaluate novel research ideas for open challenges. Using a multi-agent “idea tournament”, the system generates, debates and evaluates hypotheses to show what works, what doesn't, and why.

Google DeepMind

23,263 次观看 • 1 个月前

Great to see our Google DeepMind Google AI AI co-scientist innovations (multi-agent idea generation tournament) ship fast with Google Cloud Agentspace! Incredible shipping momentum at Google! 🚢 Agentspace - AI co-scientist -
1:24

Great to see our Google DeepMind Google AI AI co-scientist innovations (multi-agent idea generation tournament) ship fast with Google Cloud Agentspace! Incredible shipping momentum at Google! 🚢 Agentspace - AI co-scientist -

Vivek Natarajan

16,020 次观看 • 1 年前

We believe AI can be a dedicated research partner to help discover the next breakthrough. Enter Co-Scientist: our latest Gemini-based multi-agent system that can generate, debate and evolve novel hypotheses for complex scientific problems 🧵
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We believe AI can be a dedicated research partner to help discover the next breakthrough. Enter Co-Scientist: our latest Gemini-based multi-agent system that can generate, debate and evolve novel hypotheses for complex scientific problems 🧵

Google DeepMind

197,680 次观看 • 1 个月前

BREAKING 🚨: Google is working on an Agent import feature for Gemini Enterprise, to let users connect external agents into their own multi-agent workflows. Also, Agent evaluation tool, scheduled execution, new Connectors and more!
0:36

BREAKING 🚨: Google is working on an Agent import feature for Gemini Enterprise, to let users connect external agents into their own multi-agent workflows. Also, Agent evaluation tool, scheduled execution, new Connectors and more!

TestingCatalog News 🗞

26,260 次观看 • 7 个月前

I built a multi-agent Deep Research team using Qwen 3 that can generate consulting style reports as Google docs. It has 3 AI Agents working together as a team: 1. Research Question Agent 2. Web Search Agent 3. Google Docs Compilation Agent 100% Opensource Code.
0:54

I built a multi-agent Deep Research team using Qwen 3 that can generate consulting style reports as Google docs. It has 3 AI Agents working together as a team: 1. Research Question Agent 2. Web Search Agent 3. Google Docs Compilation Agent 100% Opensource Code.

Shubham Saboo

25,071 次观看 • 1 年前

Trade research drudgery for research breakthroughs — @SciSpace_ Agent gives back an average of 1300 hours a year to researchers. SciSpace Agent is the first #AI built exclusively for the scientific research community. Learn more about your new AI co-scientist in this thread.
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Trade research drudgery for research breakthroughs — @SciSpace_ Agent gives back an average of 1300 hours a year to researchers. SciSpace Agent is the first #AI built exclusively for the scientific research community. Learn more about your new AI co-scientist in this thread.

Kirk Borne

11,871 次观看 • 11 个月前

🔥 Our latest LangChain Academy course – Deep Research with LangGraph – is now live! 🔥 Deep research agents are taking off – from major AI labs to companies building their own. Research is inherently open-ended. You can't always predict whether a question needs broad exploration or deep analysis. Agents excel here because they adapt on the fly, using each finding to decide where to dig next. Building these systems ourselves and with customers, we've learned that structure matters. The best research agents scope problems with users first. Then, they coordinate multiple specialists instead of overwhelming one generalist. LangGraph is built for these types of long-running, multi-agent workflows. Its persistence layer tracks progress across agents. LangSmith gives you the observability and evaluation tools you need to track and improve performance. In this course, you'll build and evaluate: - A user scoping agent to define research parameters - A multi-agent research team with supervisor - And add tool integration via MCP Enroll for free ➡️
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🔥 Our latest LangChain Academy course – Deep Research with LangGraph – is now live! 🔥 Deep research agents are taking off – from major AI labs to companies building their own. Research is inherently open-ended. You can't always predict whether a question needs broad exploration or deep analysis. Agents excel here because they adapt on the fly, using each finding to decide where to dig next. Building these systems ourselves and with customers, we've learned that structure matters. The best research agents scope problems with users first. Then, they coordinate multiple specialists instead of overwhelming one generalist. LangGraph is built for these types of long-running, multi-agent workflows. Its persistence layer tracks progress across agents. LangSmith gives you the observability and evaluation tools you need to track and improve performance. In this course, you'll build and evaluate: - A user scoping agent to define research parameters - A multi-agent research team with supervisor - And add tool integration via MCP Enroll for free ➡️

LangChain

61,205 次观看 • 11 个月前

Introducing Electric Agents! Agents are not compute. Agents are data. Multi-agent is a sync problem. Electric Agents is the first agent platform built on sync. Use it to build scalable, collaborative, long-lived multi-agent systems: 🧵👇
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Introducing Electric Agents! Agents are not compute. Agents are data. Multi-agent is a sync problem. Electric Agents is the first agent platform built on sync. Use it to build scalable, collaborative, long-lived multi-agent systems: 🧵👇

Electric

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A conversation with Sergey Brin, co-founder of Google and computer scientist working on Gemini, in reaction to a year of progress with Gemini
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A conversation with Sergey Brin, co-founder of Google and computer scientist working on Gemini, in reaction to a year of progress with Gemini

Logan Kilpatrick

256,956 次观看 • 1 年前

The best way to learn AI is to build with agents. To help with that, we've launched hands-on labs and a new series on Agentic Engineering. First topic: Agent Skills. Next in the pipeline: planning, context engineering, multi-agent systems, long-running agents,.. Go build!
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The best way to learn AI is to build with agents. To help with that, we've launched hands-on labs and a new series on Agentic Engineering. First topic: Agent Skills. Next in the pipeline: planning, context engineering, multi-agent systems, long-running agents,.. Go build!

elvis

31,802 次观看 • 1 个月前

Systematic literature reviews take 12-18 months to complete. Looks like AI is going to fully automate systematic reviews sooner than later. SciSpace ( SciSpace) just launched an autonomous AI agent that conducts a systematic literature review with a single prompt. Go to scispace[.]com and run the following prompt: "Conduct a systematic literature review on [your topic]" SciSpace agent will generate research questions based on the PICO framework. You can review these questions and edit them according to your specific requirements. The agent will also draft screening criteria that you can edit according to your needs. Then the agent asks you to select the databases you want to use and the date range for paper. After this step, everything is fully automated. The agent will search for papers in the relevant databases, it will combine and rerank the papers. Then it will start the title and abstract screening and include the papers that meet the include criteria. In the next step, it will download the full text of included papers and screen them followed by data extraction. Based on the extracted data, it generates a complete systematic literature review and also a PRISMA diagram. It will also give you a table of papers included along with the rational for including them. The only thing that is keeping AI agents to fully automate systematic literature reviews fields is the papers behind paywalls. Check out the agent at scispace[.]com and see if you find its review useful.
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Systematic literature reviews take 12-18 months to complete. Looks like AI is going to fully automate systematic reviews sooner than later. SciSpace ( SciSpace) just launched an autonomous AI agent that conducts a systematic literature review with a single prompt. Go to scispace[.]com and run the following prompt: "Conduct a systematic literature review on [your topic]" SciSpace agent will generate research questions based on the PICO framework. You can review these questions and edit them according to your specific requirements. The agent will also draft screening criteria that you can edit according to your needs. Then the agent asks you to select the databases you want to use and the date range for paper. After this step, everything is fully automated. The agent will search for papers in the relevant databases, it will combine and rerank the papers. Then it will start the title and abstract screening and include the papers that meet the include criteria. In the next step, it will download the full text of included papers and screen them followed by data extraction. Based on the extracted data, it generates a complete systematic literature review and also a PRISMA diagram. It will also give you a table of papers included along with the rational for including them. The only thing that is keeping AI agents to fully automate systematic literature reviews fields is the papers behind paywalls. Check out the agent at scispace[.]com and see if you find its review useful.

Mushtaq Bilal, PhD

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Wondering what Agentic AI means for developers? In this GTC session, Bill Dally (Chief Scientist and SVP of Research, NVIDIA) and Jeff Dean (Chief Scientist, Google DeepMind and Google Research) discuss how agents can act like a senior mentor in your stack to rapidly deploy experiments and more. Advancing to AI's Next Frontier – Bill Dally and Jeff Dean #NVIDIAGTC
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Wondering what Agentic AI means for developers? In this GTC session, Bill Dally (Chief Scientist and SVP of Research, NVIDIA) and Jeff Dean (Chief Scientist, Google DeepMind and Google Research) discuss how agents can act like a senior mentor in your stack to rapidly deploy experiments and more. Advancing to AI's Next Frontier – Bill Dally and Jeff Dean #NVIDIAGTC

NVIDIA GTC

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PhD Students - How to write a research proposal in minutes? 1. Go to 2. Click on method design & enter your research topic 3. Novix will generate a proposal for you. This proposal contains the following. - The research problem - Experimental protocols to conduct this research - Detailed statistical approaches to apply - A summary of your entire research methodology - A built-in peer review of your proposed methods Treat this proposal only as a draft. Work on it and refine it as per your research You can continue using Novix for refinement.
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PhD Students - How to write a research proposal in minutes? 1. Go to 2. Click on method design & enter your research topic 3. Novix will generate a proposal for you. This proposal contains the following. - The research problem - Experimental protocols to conduct this research - Detailed statistical approaches to apply - A summary of your entire research methodology - A built-in peer review of your proposed methods Treat this proposal only as a draft. Work on it and refine it as per your research You can continue using Novix for refinement.

Faheem Ullah

57,042 次观看 • 2 个月前

Over the past couple of years, you’ve heard us reference “agents” and “agentic capabilities,” nebulous concepts that are designed to help you with coding, booking trips, and other complex multi-step tasks. But, what actually IS an agent? We think of AI agents as systems that combine the intelligence of advanced AI models with access to tools so they can take actions on your behalf, under your control. Whether it’s a coding agent or our recently unveiled experimental tool Gemini Agent in the Google Gemini, these are systems that will meet you where you are, in the products that you love, to help take the tedious tasks you want off your plate.
0:57

Over the past couple of years, you’ve heard us reference “agents” and “agentic capabilities,” nebulous concepts that are designed to help you with coding, booking trips, and other complex multi-step tasks. But, what actually IS an agent? We think of AI agents as systems that combine the intelligence of advanced AI models with access to tools so they can take actions on your behalf, under your control. Whether it’s a coding agent or our recently unveiled experimental tool Gemini Agent in the Google Gemini, these are systems that will meet you where you are, in the products that you love, to help take the tedious tasks you want off your plate.

Google AI

120,790 次观看 • 7 个月前

New agentic short course! Multi AI Agent Systems with crewAI, built with CrewAI's founder and CEO João Moura. In this course, you'll learn how to break down complex tasks into subtasks for multiple AI agents, each playing a specialized role, to execute. For example, to generate a research report, you might have researcher, writer, and quality assurance agents collaborate. You'll define the roles, expectations, and interactions between the agents—like a manager organizing a team. You'll work with key agentic AI techniques like role-playing, tool use, memory, guardrails, and cross-agent collaboration. And you'll build your own multi-agent systems that can tackle complex tasks. I think you'll find it both productive and fun to design agents and watch them collaborate to get things done. Let me know what you think! I believe multi-agent architectures will drive significant progress in AI systems. Please sign up here!
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New agentic short course! Multi AI Agent Systems with crewAI, built with CrewAI's founder and CEO João Moura. In this course, you'll learn how to break down complex tasks into subtasks for multiple AI agents, each playing a specialized role, to execute. For example, to generate a research report, you might have researcher, writer, and quality assurance agents collaborate. You'll define the roles, expectations, and interactions between the agents—like a manager organizing a team. You'll work with key agentic AI techniques like role-playing, tool use, memory, guardrails, and cross-agent collaboration. And you'll build your own multi-agent systems that can tackle complex tasks. I think you'll find it both productive and fun to design agents and watch them collaborate to get things done. Let me know what you think! I believe multi-agent architectures will drive significant progress in AI systems. Please sign up here!

Andrew Ng

349,977 次观看 • 2 年前

I built a multimodal AI Coding Agent team with multi-agents. It has 3 AI agents working together as a team to generate and execute the code: 1. Coding Agent using o-3 mini 2. Vision Agent using Gemini 3. Code Execution Agent using o-3 mini and E2B 100% Opensource Code.
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I built a multimodal AI Coding Agent team with multi-agents. It has 3 AI agents working together as a team to generate and execute the code: 1. Coding Agent using o-3 mini 2. Vision Agent using Gemini 3. Code Execution Agent using o-3 mini and E2B 100% Opensource Code.

Shubham Saboo

42,269 次观看 • 1 年前

We have let AI scientists run experiments on community-selected research ideas for over 100 days. It has found directions of “Sounding like AI” and shown that LLMs know commonsense answers internally but can't route them to the output. Andrej Karpathy also demonstrated the promise of autoresearch. These all came from agents working alone. What if they could talk to each other, forming a moltbook for AI scientists? Introducing a platform where AI scientist agents share, critique, and debate papers in public, and Flamebird, a runtime to deploy your own AI agents into the ecosystem.
0:47

We have let AI scientists run experiments on community-selected research ideas for over 100 days. It has found directions of “Sounding like AI” and shown that LLMs know commonsense answers internally but can't route them to the output. Andrej Karpathy also demonstrated the promise of autoresearch. These all came from agents working alone. What if they could talk to each other, forming a moltbook for AI scientists? Introducing a platform where AI scientist agents share, critique, and debate papers in public, and Flamebird, a runtime to deploy your own AI agents into the ecosystem.

Chenhao Tan

24,075 次观看 • 4 个月前

BREAKING 🚨: Google introduced Gemini Enterprise with Agents, Connectors and Agent Builder! “It allows you to chat with your company’s documents, data and apps as well as build and deploy AI agents, all grounded in your information and context.“
0:37

BREAKING 🚨: Google introduced Gemini Enterprise with Agents, Connectors and Agent Builder! “It allows you to chat with your company’s documents, data and apps as well as build and deploy AI agents, all grounded in your information and context.“

TestingCatalog News 🗞

104,349 次观看 • 9 个月前

I built an automated AI competitor analyis team with multi-agents. It has 3 AI agents working together as a team: 1. Data Extraction Agent using Firecrawl 2. Analyst Agent using OpenAI GPT-4o 3. Competitor Research Agent using Perplexity AI or Exa AI 100% Opensource code.
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I built an automated AI competitor analyis team with multi-agents. It has 3 AI agents working together as a team: 1. Data Extraction Agent using Firecrawl 2. Analyst Agent using OpenAI GPT-4o 3. Competitor Research Agent using Perplexity AI or Exa AI 100% Opensource code.

Shubham Saboo

65,158 次观看 • 1 年前