[hamdani yusuf (OP)]

Paul Roetzer Opening Keynote: The Road to AGI - MAICON 2024 (9/11/24)

Watch Paul Roetzer?s opening keynote from MAICON 2024, which took place Sept. 11, 2024. Paul explores a possible near-term future in which smarter and more powerful AI models lead to an explosion in intelligence, embodied within multi-modal language models, AI agents and robotics.

Session Abstract:

The Road to AGI: A Potential Timeline of What Happens Next, What It Means, and What We Can Do About It

For more than 70 years, researchers have pursued artificial intelligence. They were driven by a belief that we could give machines the ability to think, reason, understand, create, and take actions in the digital and physical worlds. But, progress was often slow, and the impact on our professional lives was minimal.

But, everything changed?and accelerated?with the release of ChatGPT in November 2022, and the rise of Generative AI.

Continuing AI advancements in language, vision, prediction, persuasion, reasoning, decisioning, and action will augment human capabilities and redefine knowledge work at a rate and scale that the economy has never seen.

Millions of jobs will be impacted as companies realize the power and potential of AI to drive productivity, efficiency, and profits. Every business in every industry faces the opportunity to disrupt, and the risk of being disrupted.

And this is the least capable AI we will ever have.

So, what happens next?

The future is coming faster than you may think. Be prepared to lead the transformation.

[hamdani yusuf (OP)]

Meet Nvidia?s Sana: An AI Model That Instantly Creates 4K Images!

Meet Nvidia?s Sana: An AI Model That Instantly Creates 4K Images!
Ever wondered how AI can create breathtaking 4K images in an instant? Nvidia?s new AI model, Sana, is breaking boundaries by making ultra-high-resolution image generation faster than ever before. Dive into this video to discover how Sana works, its unique features, and why it?s setting a new standard in AI image creation.

Nvidia has been at the forefront of AI innovation, and Sana is a prime example of their expertise. This model uses groundbreaking algorithms to craft 4K images, opening up opportunities for creatives, designers, and industries needing high-quality visuals. With Sana, the possibilities are limitless?from creating visuals on demand to speeding up workflows in industries that rely on image accuracy.

In this video, we?ll explore how Nvidia Sana?s AI architecture works, the cutting-edge technology behind it, and what makes it stand out from other AI models. We?ll break down the benefits for those in creative fields and dive into how this AI tool could soon become a staple for professionals worldwide. Whether you?re a designer or simply fascinated by AI advancements, this video has something for you.

Efficiency is what's expected to come next after the effectiveness has been achieved.

[hamdani yusuf (OP)]

Nvidia Finally Reveals The Future Of AI In 2025...

[hamdani yusuf (OP)]

Now is the time to go "all in" on AI (Microsoft AI's Mustafa Suleyman & Reid Hoffman) | Summit 2024

Artificial intelligence is a ?new digital species,? says Mustafa Suleyman, Microsoft AI?s CEO. For this episode, Mustafa joined Reid Hoffman on stage at the October 2024 Masters of Scale Summit. They discuss the risks and rewards of AI, and Mustafa explains why AI will change our experience of memory. Plus, why he thinks now is a great time to found and scale companies.

Chapters:
01:00 The metaphor of AI being a new digital species
03:09 Balancing creativity and caution in AI advancement
06:51 Emphasizing emotional intelligence in AI technology
08:44 The potential of voice interfaces in AI interaction
13:15 Fostering creativity through AI collaboration
15:41 The evolution of AI models, scaling, and distillation
17:15 Opportunities for entrepreneurs with small AI models
21:26 Considering the ambient sensing future
22:10 Mustafa Suleyman: Are you all in?

[hamdani yusuf (OP)]

New AI Discovery Changes Everything We Know About ChatGPTS Brain
https://x.com/dbaek__/status/1851251197744988527

[hamdani yusuf (OP)]

The Core Equation Of Neuroscience

In this video, we explore the Nobel Prize-winning Hodgkin-Huxley model, the foundational equation of computational neuroscience that reveals how neurons generate electrical signals. We break down the biophysical principles of neural computation, from membrane voltage to ion channels, showing how mathematical equations capture the elegant dance of charged particles that enables information processing.

Outline:
00:00 Introduction
01:28 Membrane Voltage
04:56 Action Potential Overview
6:24 Equilibrium potential and driving force
10:11 Voltage-dependent conductance
16:50 Review
20:09 Limitations & Outlook
21:21 Sponsor: Brilliant.org
22:44 Outro

[hamdani yusuf (OP)]

Why Scaling leads to Intelligence: Evolution and Dissipative Systems

Why do neural networks get smarter just by making them bigger? The answer lies in physics, specifically in what we call "dissipative systems" - the same principle that drives evolution and complexity in nature. I use these insights to make predictions of the direction AI will go, and how these intuitions lead us to conclude that the scaling law ensures a path towards artificial general intelligence (AGI).

This way of thinking is similar to complexity theory. I will soon make a video explaining how this applies to neural networks specifically as it's a fascinating subject.

I regularly upload valuable insights from recent papers like this one. Subscribe to stay up-to-date with the latest understanding of AI models.

Paper: Dissipative Structures, Organisms and Evolution
Authors: Dilip K Kondepudi 1,2,*, Benjamin De Bari 2,3, James A Dixon 2,3
Link: https://pmc.ncbi.nlm.nih.gov/articles...

Research from Google on evolutionary neural networks:
https://research.google/blog/using-ev...

Timestamps:
00:00 - The bitter lesson: explained by dissipative systems
01:17 - 3 Prerequisites for Emergence of Complexity
01:50 - 1. Selection
02:30 - 2. Diversity (mutation)
04:32 - 3. Energy (and dissipative systems)
06:02 - Why scale will lead to intelligence
06:53 - Scale will lead to more general AI
09:14 - Removing constraints so AI can become general
10:10 - More general reward function
11:00 - More general architectures
12:09 - Conclusion: Scale is all we need

[hamdani yusuf (OP)]

Here's a video precluding the last one, addressing an issue from a research paper.

Paper shows Scale alone won't lead to AGI: Scaling Laws don't apply to OOD Tasks

OOD = out-of-distribution. In this context, we refer to OOD data as data that is sufficiently different from the models' training data.

I regularly upload valuable insights from recent papers like this one. Subscribe to stay up-to-date with the latest understanding of AI models.

This fascinating paper (https://arxiv.org/abs/2406.06489) shows that machine learning models (like transformer-based large language models) don't generalize to OOD tasks. Performance for these OOD tasks does not improve with scale, meaning scaling laws might not work for this subset of tasks. Therefore, scale alone might not be enough to reach AGI, as OOD tasks are a core part of general intelligence.

In this video, I argue why this is more nuanced than it seems, provide thoughts and insights on how this paper relates to previous research about grokking and finally offer a possible solution to this problem.
 
Title: Probing out-of-distribution generalization in machine learning for materials
Authors: Kangming Li, Andre Niyongabo Rubungo, Xiangyun Lei, Daniel Persaud, Kamal Choudhary, Brian DeCost, Adji Bousso Dieng, Jason Hattrick-Simpers
Linkt to Paper: https://arxiv.org/abs/2406.06489

Timestamps:
00:00 - Problem introduction
01:02 - The paper's claim
01:35 - Grokking counter-example
02:18 - Difference Paper vs. Grokking papers: Problem Complexity
03:43 - Data Diversity leads to Generalization
05:28 - Formula for Generalization
05:48 - Scaling Laws and OOD tasks
07:07 - Inversed Scaling Law due to Regularization
07:44 - Simple Underlying Rules (problem complexity) lead to Generalization: Formula
09:33 - The solution: The human way
10:53 - Validation leads to Generalizing OOD

[hamdani yusuf (OP)]

This is a more practical use case of AI in common office workplaces.

You Won?t Believe What Excel?s Copilot Can Do! (new updates)

Using Microsoft 365 just got even better with the latest Copilot AI features in Excel. Now, you can analyze and organize data with ease. Here?s what Microsoft Copilot can do for you:

1. Summarize Text: Save time by letting Copilot review customer feedback or other text data. Get instant summaries that highlight the main points.
2. Gain Quick Insights: Just ask! Copilot Microsoft can find patterns and details in your data in seconds.
3. Create Formulas with a Click: Copilot AI suggests formulas you might need, like calculating years of service. It?s fast and easy.
4. Look Up Data: Have data on another sheet? Copilot can pull it into your main sheet for quick access.
5. Highlight Important Details: Use Copilot to apply colors to your data, like flagging duplicate entries or marking high-performance rows.

Want to boost your Excel skills even more? Check out our beginner-friendly courses. Every course is designed to help you learn Excel?s most powerful tools, step by step 👉 https://link.xelplus.com/yt-d-all-cou...

00:00 How to Use Copilot in Excel - Tutorial
00:31 Summarize Text
03:32 Get Insights on Data
05:00 Write Formulas
06:28 Lookup Data
07:54 Highlight Duplicate Values
08:52 Conditional Formatting - Highlight Entire Rows
09:53 Learn Excel, step by step

[hamdani yusuf (OP)]

New AI Humanoid Robot - IRON - Is Made to Replace Humans

XPeng's 2024 AI Day showcased groundbreaking advancements in robotics, AI chips, and flying cars. The event introduced Iron, a humanoid robot powered by XPeng's Turing AI chip, now actively working on XPeng's production line and designed for future roles in retail and offices. Alongside the robot, XPeng revealed its high-efficiency Kunpeng Super Electric System, ultra-fast EV charging, and plans for urban flying cars, signaling a bold move to lead in AI-driven technology across multiple sectors.

Key Topics Covered: 
XPeng?s Humanoid Robot Iron: A next-gen robot actively working on XPeng's factory floor 
How XPeng?s Turing AI Chip powers Iron, autonomous vehicles, and potentially flying cars 
The potential impact of XPeng?s AI-driven tech on robotics, EVs, and urban mobility 

🎥 What You?ll Learn: 
How XPeng?s robot Iron combines advanced AI and precision to work alongside humans 
Why the Turing AI Chip marks a major leap in robotics, EV efficiency, and autonomous driving 
How XPeng's innovations in flying cars and high-speed EV charging could redefine future transportation 

📊 Why This Matters: 
This video explores XPeng's latest AI-driven innovations, from the humanoid robot Iron to their ultra-efficient Kunpeng power system, which aims to extend EV range and enable ultra-fast charging. With ambitious projects like robotaxis and modular flying cars, XPeng is pushing the boundaries of AI, robotics, and sustainable mobility on a global scale. 

[hamdani yusuf (OP)]

Nvidias NEW Robotics Breakthroughs Accelerates physics by 10,000x (Nvidia Hover)

Welcome to my channel where i bring you the latest breakthroughs in AI. From deep learning to robotics, i cover it all. My videos offer valuable insights and perspectives that will expand your knowledge and understanding of this rapidly evolving field. Be sure to subscribe and stay updated on my latest videos.

[alancalverd]

New AI Humanoid Robot - IRON - Is Made to Replace Humans

We already have too many humans. Why make a replacement?

[hamdani yusuf (OP)]

New AI Humanoid Robot - IRON - Is Made to Replace Humans

We already have too many humans. Why make a replacement?

Because they can be better, safer, faster, cheaper alternative to perform tasks usually done by humans.

[hamdani yusuf (OP)]

Microsoft?s New Magnetic-One AI System Is More Powerful Than Anything We?ve Seen!

Microsoft's Magnetic-One AI is a powerful, multi-agent system designed to handle complex tasks by using specialized agents for web browsing, file management, coding, and executing commands. This AI system, led by a central Orchestrator, can seamlessly perform a range of activities, from booking tickets to analyzing data, making it highly adaptable and efficient. Built on Microsoft?s open-source AutoGen framework, Magnetic-One stands out as a flexible, action-oriented AI that?s pushing the boundaries of technology.

Key Topics Covered: 
Microsoft?s Magnetic-One AI System: A multi-agent AI powerhouse that tackles complex tasks effortlessly 
How Magnetic-One uses specialized agents to perform tasks like web browsing, coding, and file management 
The potential impact of Magnetic-One on productivity, automation, and the future of AI technology 

🎥 What You?ll Learn: 
How Microsoft?s Magnetic-One AI system combines multiple agents to create a super-efficient task manager 
Why Magnetic-One is a significant step forward in building versatile, action-oriented AI systems 
How Magnetic-One?s modular design allows it to adapt to a variety of tasks, from everyday tasks to specialized technical operations 

📊 Why This Matters: 
This video explores Microsoft?s Magnetic-One AI system, a groundbreaking advancement in multi-agent AI that moves beyond simple responses to performing complex actions autonomously. From boosting productivity to reshaping automation, Magnetic-One could redefine how AI integrates into our daily and professional lives.

[hamdani yusuf (OP)]

Why o1 is a BIG deal

While most are thinking of the next architecture for AI models, OpenAI seems to have found its solution: o1.

In this video, I'll use system 1 and system 2 theory from D. Kahneman's book 'Thinking Fast and Slow' to analyze o1. Consequently, I'll show that with the arrival of this new model, the difference between human and AI reasoning seems to have dissolved.

*Quick Disclaimer*: I'm not talking about o1 as in the current o1- (preview)model; I agree that this one is still subpar compared to humans in many domains. Instead, I'm talking about the trajectory of these o1-type models.

Timestamps:
00:00 - OpenAI's CEO on o1 and AGI
02:02 - Empirical evidence on o1-preview
02:25 - Understanding o1 from a psychological perspective
03:16 - System 1: An example
03:34 - System 1: Models vs. humans
04:59 - System 1: A psychological trick
05:20 - System 1: Generalizing to new data
06:16 - System 2: The paradigm shift
06:50 - System 2: What is reasoning?
07:38 - System 2: Explaining using an example
09:35 - System 2: Old models' reasoning
10:05 - System 2: o1 is trained differently
11:18 - Humans and AI are no longer different
12:26 - o1 is at GPT-2 level of reasoning
12:46 - Can you feel it?
13:16 - Missing ingredient: Active learning
13:46 - Solution to active learning
14:18 - No more roadblocks ahead

[alancalverd]

Because they can be better, safer, faster, cheaper alternative to perform tasks usually done by humans.

We already have robots that can do this, but they are not humanoid. The problem with humans, and humanoids in general, is that bipedal locomotion, or even standing still on two feet,  requires an awful lot of energy and computing power. It's pretty good for covering rough ground, though quadrupeds are much more efficient, but a complete waste of effort on a flat surface. So the ultimate robot would either have wheels or tracks, or be more canine than humanoid.

[hamdani yusuf (OP)]

Because they can be better, safer, faster, cheaper alternative to perform tasks usually done by humans.

We already have robots that can do this, but they are not humanoid. The problem with humans, and humanoids in general, is that bipedal locomotion, or even standing still on two feet,  requires an awful lot of energy and computing power. It's pretty good for covering rough ground, though quadrupeds are much more efficient, but a complete waste of effort on a flat surface. So the ultimate robot would either have wheels or tracks, or be more canine than humanoid.

The reason for building humanoid robots is for versatility. Working environments have been optimized for median human bodies. If they are expected to do everything that human individuals can do, then having the same physical form factor is an obvious starting point.
 
Those humanoid robots are designed to have capability to use tools like humans do. If they are assigned to a more specific tasks, they can simply use the more appropriate tools. They can even replace their standard legs with wheels or hands with 3D printing nozzles. Those things would be hard to do with humans.

[alancalverd]

But if it had 3 or 4 legs, and more eyes, it could do everything better than a human.

Or to take a simple case of "optimised" working conditions, "garrow" coal cutters - machines that can mine coal from very thin seams without having to excavate the tons of rock needed to accommodate a human.

Or a complicated case: a Mars lander that can tolerate significant cosmic radiation and, instead of dying when it gets cold, just  hibernate until the sun shines again.

Surely the object of a robot is to do stuff that humans can't? So why make it as big, clumsy and inefficient as a human?

[hamdani yusuf (OP)]

But if it had 3 or 4 legs, and more eyes, it could do everything better than a human

Not necessarily. It would weigh more, and take more space.

[hamdani yusuf (OP)]

Surely the object of a robot is to do stuff that humans can't? So why make it as big, clumsy and inefficient as a human?

Previous generations of robots can already do some tasks better than humans. But there are some tasks they still can't do. That's where human workers are still needed.
The humanoid robots are intended to fill the gap.