Загрузка видео...

Не удалось загрузить видео

На главную

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

SentientAGI's profile picture

Sentient

508,035 subscribers

13,087 просмотров • 8 месяцев назад •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 27

Фото профиля dave
dave8 месяцев назад

keep building!

Фото профиля Nex Drop
Nex Drop8 месяцев назад

Coordination between agents is still early

Фото профиля ҡσʝσ😎
ҡσʝσ😎8 месяцев назад

open-source agi will win sentient will win😌

Фото профиля Neb⊙lisa Akimitsu 🍚 ⛓
Neb⊙lisa Akimitsu 🍚 ⛓8 месяцев назад

Community wins with Sentient

Фото профиля hieunguyen
hieunguyen8 месяцев назад

A single super AI is no match for a network of agents working together as a team. The leap to AGI will come from the ability to coordinate workflows rather than simply increasing data.

Фото профиля Alex Cryptan
Alex Cryptan8 месяцев назад

reasoning beats raw model size in the real world

Фото профиля Bafspot
Bafspot8 месяцев назад

Sentient is the future of open AI

Фото профиля Eudomania
Eudomania8 месяцев назад

Amazing 👌🏾 Making Open source AI win

Фото профиля Bitplanet
Bitplanet8 месяцев назад

Reasoning improves when agents share context, align incentives, and operate within well designed systems.

Фото профиля Moory
Moory8 месяцев назад

where do you see $BONDUM in an AI world?

Фото профиля 0x_Vivek
0x_Vivek8 месяцев назад

reasoning needs better data, not just better systems lol

Фото профиля DOTIBE
DOTIBE8 месяцев назад

Following this path makes our AGI solve real world problems

Фото профиля Doxxed🎒
Doxxed🎒8 месяцев назад

Which is why I trust that sentient will make a huuge difference in the world of AGI

Фото профиля Outlandish Szn Is Close | π
Outlandish Szn Is Close | π8 месяцев назад

Keep building

Фото профиля Merlin 🪄 🚢
Merlin 🪄 🚢8 месяцев назад

With Sentient, problem solve easily

Фото профиля Betta
Betta8 месяцев назад

Here to contribute heavily

Фото профиля 币猎.eth🐑
币猎.eth🐑8 месяцев назад

agents are cool, coordination is king, but without hard evals and skin in the game it’s demo-ware xD ship harder!

Фото профиля jinli锦鲤 |🧠SENT
jinli锦鲤 |🧠SENT8 месяцев назад

Keep going!

Фото профиля Prey.gdp
Prey.gdp8 месяцев назад

Season 2 Season 2 Season 2

Фото профиля SANJU
SANJU8 месяцев назад

Yes yes yes yes this is 100% right

Фото профиля 0xbrah〽️
0xbrah〽️8 месяцев назад

🫡

Фото профиля Datweb3guy
Datweb3guy8 месяцев назад

Open source AGI ontop

Фото профиля Moral_Hazard
Moral_Hazard8 месяцев назад

Real ai infra

Фото профиля Sky
Sky8 месяцев назад

ai systems are making progress, but still struggle with complex real-world problems. the path forward requires advances in reasoning, workflow integration, and multi-agent coordination

Фото профиля roman
roman8 месяцев назад

agi It's the future

Фото профиля 0xTheDanielS✜
0xTheDanielS✜8 месяцев назад

Open Source keeps winning

Фото профиля deathmage.x .edge🦭
deathmage.x .edge🦭8 месяцев назад

그럼 아직 멀었네요 ㅎㅎ

Похожие видео

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.
0:45

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

15,287 просмотров • 8 месяцев назад

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
1:42

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

648,875 просмотров • 2 лет назад

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:
0:35

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

602,799 просмотров • 2 лет назад

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”
0:53

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

30,573 просмотров • 8 месяцев назад

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
1:05

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 просмотров • 8 месяцев назад

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
1:09

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.

144,040 просмотров • 11 месяцев назад

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
13:23

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 просмотров • 4 месяцев назад

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
1:48:04

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

58,117 просмотров • 2 месяцев назад

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:
3:51

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 просмотров • 1 год назад

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:
3:51

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

861,524 просмотров • 1 год назад

$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.
13:30

$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 просмотров • 7 месяцев назад

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.
1:47

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 просмотров • 1 год назад

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"
2:07

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 просмотров • 7 месяцев назад

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:
0:58

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

20,074 просмотров • 1 год назад

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.
13:23

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

471,988 просмотров • 4 месяцев назад

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.
1:55

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 просмотров • 8 месяцев назад

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:
0:38

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 просмотров • 1 год назад

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.
5:06

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 просмотров • 6 месяцев назад