Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

Current AI cannot solve difficult, real-world problems. The path to AGI is a focus on improving reasoning systems, multi agent workflows, and coordination.

Sentient

533,505 subscribers

13,073 views • 7 months ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

Money is the coordination layer for agents. As multi-agent systems take on real tasks, they will need to quote, budget, pay, and settle as part of their reasoning loop. We are building this reasoning infrastructure in the open, for everyone.

Money is the coordination layer for agents. As multi-agent systems take on real tasks, they will need to quote, budget, pay, and settle as part of their reasoning loop. We are building this reasoning infrastructure in the open, for everyone.

Sentient

14,712 views • 7 months ago

Education needs a major shift—current systems aren’t preparing students for the real world. The future is immersive, AI-enabled metaverse, where learning happens by solving real-world problems and getting rewarded for it. 🚀 #FutureOfLearning #AI #CEEK #Metaverse #SkillsTransfer

Education needs a major shift—current systems aren’t preparing students for the real world. The future is immersive, AI-enabled metaverse, where learning happens by solving real-world problems and getting rewarded for it. 🚀 #FutureOfLearning #AI #CEEK #Metaverse #SkillsTransfer

Ceek

648,866 views • 2 years ago

I'm partnering with Mike Knoop to launch ARC Prize: a $1,000,000 competition to create an AI that can adapt to novelty and solve simple reasoning problems. Let's get back on track towards AGI. Website: ARC Prize on Kaggle:

I'm partnering with Mike Knoop to launch ARC Prize: a $1,000,000 competition to create an AI that can adapt to novelty and solve simple reasoning problems. Let's get back on track towards AGI. Website: ARC Prize on Kaggle:

François Chollet

602,549 views • 2 years ago

Bharat’s Focus: AI for Real Impact ✅ AI for solving real world problems ✅ AI for improving enterprise productivity ✅ AI for population-scale problems: healthcare, agriculture, climate change 📍Research Symposium on “AI & its Impact”

Bharat’s Focus: AI for Real Impact ✅ AI for solving real world problems ✅ AI for improving enterprise productivity ✅ AI for population-scale problems: healthcare, agriculture, climate change 📍Research Symposium on “AI & its Impact”

Ashwini Vaishnaw

30,566 views • 6 months ago

Mistral CEO Arthur Mensch on why AGI is too simple of a concept: “There’s no such thing as one system that is going to be solving all of the problems of the world... It’s never going to exist.” “Just like you don't have any human that is able to solve every task in the world, you of course have to have some level of specialization to actually solve problems.” “We are back from magical thinking to system thinking.” “In real life enterprises are just complex systems. You can’t solve that with a single abstraction, which is AGI.” Arthur Mensch on Big Technology Podcast

Mistral CEO Arthur Mensch on why AGI is too simple of a concept: “There’s no such thing as one system that is going to be solving all of the problems of the world... It’s never going to exist.” “Just like you don't have any human that is able to solve every task in the world, you of course have to have some level of specialization to actually solve problems.” “We are back from magical thinking to system thinking.” “In real life enterprises are just complex systems. You can’t solve that with a single abstraction, which is AGI.” Arthur Mensch on Big Technology Podcast

a16z

37,667 views • 7 months ago

Demis Hassabis says Gemini 3 is on track and shows the fastest progress in the industry But general intelligence requires more than just the current trajectory It needs better reasoning, stronger memory, and "world model ideas" to solve physical intelligence AGI is still 5–10 years away

Demis Hassabis says Gemini 3 is on track and shows the fastest progress in the industry But general intelligence requires more than just the current trajectory It needs better reasoning, stronger memory, and "world model ideas" to solve physical intelligence AGI is still 5–10 years away

Haider.

143,920 views • 9 months ago

Anthropic Managed Agents team: "Fable 5 - is our best model for self-improving agent systems. add /loops, dynamic workflows, dreaming - and you're unstoppable." in 13 minutes, the Anthropic team shows how to build self-improving agent systems with Fable 5 from scratch. Worth more than a $500 agent-building course. Live from the Anthropic stage in Japan. Unpublished

Anthropic Managed Agents team: "Fable 5 - is our best model for self-improving agent systems. add /loops, dynamic workflows, dreaming - and you're unstoppable." in 13 minutes, the Anthropic team shows how to build self-improving agent systems with Fable 5 from scratch. Worth more than a $500 agent-building course. Live from the Anthropic stage in Japan. Unpublished

Movez

67,091 views • 2 months ago

Yann leCun — Current AI systems lack real-world understanding, reasoning, and memory. However, within the next decade or two, AI will surpass human intelligence, gaining common sense, and safety guardrails to stay under control while completing tasks.

Yann leCun — Current AI systems lack real-world understanding, reasoning, and memory. However, within the next decade or two, AI will surpass human intelligence, gaining common sense, and safety guardrails to stay under control while completing tasks.

Haider.

81,286 views • 1 year ago

Do not spend 2 years learning AI agents the slow way Andrew Ng just shared a complete 2-hour roadmap On becoming an agentic AI engineer in 2026: 00:00 - Learn the foundations of AI agents 12:12 - Design agentic workflows 53:27 - Build agents that work in practice 1:20:30 - Create self-improving loops 1:30:19 - Orchestrate multi-agent systems Most people are still learning how to prompt one model Andrew Ng is already teaching the complete stack: Agents → Workflows → Loops → Multi-Agent Systems Prompting is the old workflow Building autonomous systems is the new one Anthropic pays top engineers up to $750K a year to understand this stack Bookmark this and give it two hours today Then read the full graph engineering guide below

Do not spend 2 years learning AI agents the slow way Andrew Ng just shared a complete 2-hour roadmap On becoming an agentic AI engineer in 2026: 00:00 - Learn the foundations of AI agents 12:12 - Design agentic workflows 53:27 - Build agents that work in practice 1:20:30 - Create self-improving loops 1:30:19 - Orchestrate multi-agent systems Most people are still learning how to prompt one model Andrew Ng is already teaching the complete stack: Agents → Workflows → Loops → Multi-Agent Systems Prompting is the old workflow Building autonomous systems is the new one Anthropic pays top engineers up to $750K a year to understand this stack Bookmark this and give it two hours today Then read the full graph engineering guide below

rari

57,616 views • 28 days ago

Introducing NEO: The first Agentic Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on the MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:

Introducing NEO: The first Agentic Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on the MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:

Neo AI

19,571,775 views • 1 year ago

$NOW CEO Bill McDermott says AI can identify problems but it cannot fix them. ServiceNow sits on 80B enterprise workflows across legacy systems and acts as the “last-mile” execution layer.

$NOW CEO Bill McDermott says AI can identify problems but it cannot fix them. ServiceNow sits on 80B enterprise workflows across legacy systems and acts as the “last-mile” execution layer.

Shay Boloor

137,777 views • 5 months ago

Introducing NEO: The first Autonomous Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34.2% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on OpenAI's MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:

Introducing NEO: The first Autonomous Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34.2% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on OpenAI's MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:

Neo AI

861,393 views • 1 year ago

The 18th Annual AGI Conference (AGI-25) took place last month at Reykjavik University in Iceland, bringing together participants from over 80 countries to explore pioneering research on the path to AGI. AGI-25 programming featured distinguished keynote presentations from leaders in academia and industry, including Ben Goertzel, Richard Sutton, Tatiana Shavrina, Michael Levin, Anna Ciaunica PhD @annaciaunica.bsky.social, and Joscha Bach. These talks addressed core challenges in AGI theory and implementation, the limitations of current state-of-the-art learning systems, and new frameworks for integrating reasoning, embodiment, and autonomy. The accepted papers covered a broad range of research areas, including novel learning algorithms, reasoning systems, theoretical neurobiology and bio-inspired systems, quantum computing, theories of machine consciousness, ethics, safety, formal mathematical foundations, and philosophy of AGI. The conversation on advancing general machine intelligence continues toward AGI-26.

The 18th Annual AGI Conference (AGI-25) took place last month at Reykjavik University in Iceland, bringing together participants from over 80 countries to explore pioneering research on the path to AGI. AGI-25 programming featured distinguished keynote presentations from leaders in academia and industry, including Ben Goertzel, Richard Sutton, Tatiana Shavrina, Michael Levin, Anna Ciaunica PhD @annaciaunica.bsky.social, and Joscha Bach. These talks addressed core challenges in AGI theory and implementation, the limitations of current state-of-the-art learning systems, and new frameworks for integrating reasoning, embodiment, and autonomy. The accepted papers covered a broad range of research areas, including novel learning algorithms, reasoning systems, theoretical neurobiology and bio-inspired systems, quantum computing, theories of machine consciousness, ethics, safety, formal mathematical foundations, and philosophy of AGI. The conversation on advancing general machine intelligence continues toward AGI-26.

SingularityNET

32,589 views • 11 months ago

Joscha Bach says current AI doesn't need to be conscious to execute tasks, but it must simulate reality to understand it LLMs don't just predict words To solve complex problems, they build deep internal models of spatial reasoning and the human theory of mind "that is why AI can talk about reasoning and mental states"

Joscha Bach says current AI doesn't need to be conscious to execute tasks, but it must simulate reality to understand it LLMs don't just predict words To solve complex problems, they build deep internal models of spatial reasoning and the human theory of mind "that is why AI can talk about reasoning and mental states"

Haider.

17,743 views • 6 months ago

The challenge: Build an agent with Agentforce to solve a real-world business problem. The twist: They only had a halftime to do it. 🏈⚡ See how fast it is to create your first AI agent on Trailhead:

The challenge: Build an agent with Agentforce to solve a real-world business problem. The twist: They only had a halftime to do it. 🏈⚡ See how fast it is to create your first AI agent on Trailhead:

Salesforce

20,062 views • 1 year ago

Banks, governments, and telecoms must keep data secure. This is where Sentient comes in with ROMA orchestrating multi-agent workflows, while the GRID supplies open-models and tools they can run on-prem. This is how open-source AI reasoning gets deployed into constrained environments.

Banks, governments, and telecoms must keep data secure. This is where Sentient comes in with ROMA orchestrating multi-agent workflows, while the GRID supplies open-models and tools they can run on-prem. This is how open-source AI reasoning gets deployed into constrained environments.

Sentient

19,513 views • 7 months ago

Anthropic Managed Agents team: "Fable 5 is our best model for running self-improving agent systems. Add /loops, dynamic workflows, dreaming and you are unstoppable" in 13-minutes, Anthropic team shows how to build self-improving agent systems with Fable 5 from scratch. Worth more than a $500 agent building course. Live from the last Anthropic stage in Japan. Unpublished.

Anthropic Managed Agents team: "Fable 5 is our best model for running self-improving agent systems. Add /loops, dynamic workflows, dreaming and you are unstoppable" in 13-minutes, Anthropic team shows how to build self-improving agent systems with Fable 5 from scratch. Worth more than a $500 agent building course. Live from the last Anthropic stage in Japan. Unpublished.

Codez

471,265 views • 2 months ago

It is now way easier to build multi-agent workflows in n8n with the new AI Agent Tool node. Instead of juggling sub workflows a parent AI Agent can now use other Agents as tools even several layers deep. Max shows how a cheap GPT 4.1 nano Agent handles heavy data and sends summaries to a parent Agent saving token costs and making debugging simpler. Watch the full walkthrough:

It is now way easier to build multi-agent workflows in n8n with the new AI Agent Tool node. Instead of juggling sub workflows a parent AI Agent can now use other Agents as tools even several layers deep. Max shows how a cheap GPT 4.1 nano Agent handles heavy data and sends summaries to a parent Agent saving token costs and making debugging simpler. Watch the full walkthrough:

n8n.io

27,808 views • 1 year ago

General AI is already a dated concept. ThinkingAI connects an entire business ecosystem through MCP, pulling data, tools, and workflows into a single execution layer. Not isolated AI tools working separately. Coordinated agent systems triggering real actions across the whole operation. The shift is from using AI to running on it. Every company becomes an AI-powered system built on custom agent teams through the ThinkingAI Agentic Engine.

General AI is already a dated concept. ThinkingAI connects an entire business ecosystem through MCP, pulling data, tools, and workflows into a single execution layer. Not isolated AI tools working separately. Coordinated agent systems triggering real actions across the whole operation. The shift is from using AI to running on it. Every company becomes an AI-powered system built on custom agent teams through the ThinkingAI Agentic Engine.

Future Stacked

52,003 views • 4 months ago