Loading video...

Video Failed to Load

Go Home

"The localized nature of predictive coding is a big benefit because the semantics tend to modularize better." Our CEO, Dr. Ben Goertzel, on improving neural-symbolic integration with predictive coding.

ASI_Alliance's profile picture

Artificial Superintelligence Alliance

97,125 subscribers

11,575 views • 3 months ago •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

.@replit CEO Amjad Masad on vibe coding: "We started our company even 10 years ago before vibe coding, with the express mission of creating a billion new software developers. Now, with the rise of AI, what happened is AI turned out to be really good at coding." #AxiosAISummit
1:43

.@replit CEO Amjad Masad on vibe coding: "We started our company even 10 years ago before vibe coding, with the express mission of creating a billion new software developers. Now, with the rise of AI, what happened is AI turned out to be really good at coding." #AxiosAISummit

Axios

32,814 views • 6 months ago

Elon Musk: A lot of people have spiritual feelings and I wouldn’t deny those feelings are real to them. But just because someone else has a spiritual moment doesn’t mean I’ll have the same one. I tend to be physics-pulled — if something has predictive value, I pay attention. Physics is the study of that which has predictive value.
0:54

Elon Musk: A lot of people have spiritual feelings and I wouldn’t deny those feelings are real to them. But just because someone else has a spiritual moment doesn’t mean I’ll have the same one. I tend to be physics-pulled — if something has predictive value, I pay attention. Physics is the study of that which has predictive value.

Ian Miles Cheong

65,635 views • 9 months ago

Satya Nadella says the convergence of quantum computing and AI will unlock the language of nature Because nature is inherently quantum, quantum computers are the ultimate tool to generate training data for AI models "this enables predictive simulations, like testing cancer immunotherapies"
1:09

Satya Nadella says the convergence of quantum computing and AI will unlock the language of nature Because nature is inherently quantum, quantum computers are the ultimate tool to generate training data for AI models "this enables predictive simulations, like testing cancer immunotherapies"

Haider.

17,148 views • 6 months ago

New podcast on vibe coding - A Return to Code. A Return to Coding 00:20 The Personal App Store 03:17 Vibe Coding Is a Video Game with Real-World Rewards 06:22 Pure Software Is Uninvestable 10:33 A Place for Each Model 14:22 AI Is Eager to Please 17:57 Why Math and Coding? 22:10 The Beginning of the End of Apple’s Dominance 24:17 Coding Agents As Customer Service Reps 27:55
29:36

New podcast on vibe coding - A Return to Code. A Return to Coding 00:20 The Personal App Store 03:17 Vibe Coding Is a Video Game with Real-World Rewards 06:22 Pure Software Is Uninvestable 10:33 A Place for Each Model 14:22 AI Is Eager to Please 17:57 Why Math and Coding? 22:10 The Beginning of the End of Apple’s Dominance 24:17 Coding Agents As Customer Service Reps 27:55

Naval

631,321 views • 4 months ago

Gitlip Editors: Demo Launch 🌷 Gitlip is a collaborative coding platform, and today we’re launching the demo for Gitlip Editors - our take on collaborative coding environments. If you've ever wanted to collaborate on Markdown or code with the convenience of Google Docs, our demo is for you.
0:34

Gitlip Editors: Demo Launch 🌷 Gitlip is a collaborative coding platform, and today we’re launching the demo for Gitlip Editors - our take on collaborative coding environments. If you've ever wanted to collaborate on Markdown or code with the convenience of Google Docs, our demo is for you.

Natalie Marleny

35,886 views • 1 year ago

Lead of Claude Code: "100% of our code at Anthropic is now shipped by Claude. Over 30% of it is written using /loops. Loop engineering is the future of coding. Loops are as big a shift as move from source coding to agents" in a 1-hour podcast, a Claude Code lead engineer shares the internal setup for building the #1 coding tool. It’s the most useful breakdown of how the Anthropic team uses Claude internally that I’ve seen on YouTube. Worth more than a $500 vibe-coding course.
1:37:24

Lead of Claude Code: "100% of our code at Anthropic is now shipped by Claude. Over 30% of it is written using /loops. Loop engineering is the future of coding. Loops are as big a shift as move from source coding to agents" in a 1-hour podcast, a Claude Code lead engineer shares the internal setup for building the #1 coding tool. It’s the most useful breakdown of how the Anthropic team uses Claude internally that I’ve seen on YouTube. Worth more than a $500 vibe-coding course.

Codez

37,770 views • 3 months ago

Our CEO, Dr. Ben Goertzel, and Kai Zen Bickle, Chief Ethics Officer at BGI Foundry, sat down with Julian Issa on The Beyond Tomorrow Podcast to discuss AGI timelines, recursive self-improvement, existential risk, the Hyperon framework, ASI:Chain, and OmegaClaw.
0:59

Our CEO, Dr. Ben Goertzel, and Kai Zen Bickle, Chief Ethics Officer at BGI Foundry, sat down with Julian Issa on The Beyond Tomorrow Podcast to discuss AGI timelines, recursive self-improvement, existential risk, the Hyperon framework, ASI:Chain, and OmegaClaw.

Artificial Superintelligence Alliance

17,125 views • 2 months ago

Introducing PC-ALM, a local-learning alternative to backpropagation. Our method trains 1000-layer neural nets using only local dynamics, and without backprop. Blog: Standard deep learning relies on backpropagation. The brain, however, cannot implement backpropagation, at least not exactly. How can a physical system, such as the brain, solve multilayer credit assignment without explicit use of backprop? We look for inspiration in two related fields: distributed optimization and NeuroAI. In NeuroAI, predictive coding asks each neuron activation to solve an energy-based inference problem instead of using a standard forward pass. That inference step can be implemented as energy-minimization dynamics on local prediction errors. This perspective -- each layer as a dynamical system -- has proven promising, but performance of predictive coding hasn't scaled well with depth. Credit signals at far ends of the network struggle to diffuse into internal layers. We turn to distributed optimization, generalizing predictive coding to use an augmented Lagrangian instead of energy. This motivation stems back to a classic 1988 paper by LeCun, showing that the Lagrange multipliers of a deep network can be identified with gradients of a supervised loss. The augmented Lagrangian then bridges LeCun's perspective to the standard predictive coding that is used in NeuroAI. We find that this new perspective yields a natural PC-like alternative to backpropagation, resulting in a method we call PC-ALM. PC-ALM differs from PC in that it introduces dual neurons (Lagrange multipliers) as part of the layer-local dynamics, resulting in each layer acting as a PI feedback control system to minimize local prediction errors. We find that PC-ALM is capable of propagating signals to seemingly arbitrary depth, especially in deep narrow networks where standard PC struggles to learn. Ultimately, our motivation here is to understand how distributed physical systems, such as the brain, can compute credit signals using only local coupling and local dynamics. PC-ALM may also inform deep learning in neuromorphic hardware, where dynamics are cheaper than on GPUs. Paper: Code:
0:45

Introducing PC-ALM, a local-learning alternative to backpropagation. Our method trains 1000-layer neural nets using only local dynamics, and without backprop. Blog: Standard deep learning relies on backpropagation. The brain, however, cannot implement backpropagation, at least not exactly. How can a physical system, such as the brain, solve multilayer credit assignment without explicit use of backprop? We look for inspiration in two related fields: distributed optimization and NeuroAI. In NeuroAI, predictive coding asks each neuron activation to solve an energy-based inference problem instead of using a standard forward pass. That inference step can be implemented as energy-minimization dynamics on local prediction errors. This perspective -- each layer as a dynamical system -- has proven promising, but performance of predictive coding hasn't scaled well with depth. Credit signals at far ends of the network struggle to diffuse into internal layers. We turn to distributed optimization, generalizing predictive coding to use an augmented Lagrangian instead of energy. This motivation stems back to a classic 1988 paper by LeCun, showing that the Lagrange multipliers of a deep network can be identified with gradients of a supervised loss. The augmented Lagrangian then bridges LeCun's perspective to the standard predictive coding that is used in NeuroAI. We find that this new perspective yields a natural PC-like alternative to backpropagation, resulting in a method we call PC-ALM. PC-ALM differs from PC in that it introduces dual neurons (Lagrange multipliers) as part of the layer-local dynamics, resulting in each layer acting as a PI feedback control system to minimize local prediction errors. We find that PC-ALM is capable of propagating signals to seemingly arbitrary depth, especially in deep narrow networks where standard PC struggles to learn. Ultimately, our motivation here is to understand how distributed physical systems, such as the brain, can compute credit signals using only local coupling and local dynamics. PC-ALM may also inform deep learning in neuromorphic hardware, where dynamics are cheaper than on GPUs. Paper: Code:

Sakana AI

437,915 views • 11 days ago

Palantir CEO Dr. Alex Karp talking about their original deal with Airbus: “What we’ve been able to do together is really begin to change the underlying economics of flying— by having predictive idea of which parts are likely to fail before they fail.”
0:34

Palantir CEO Dr. Alex Karp talking about their original deal with Airbus: “What we’ve been able to do together is really begin to change the underlying economics of flying— by having predictive idea of which parts are likely to fail before they fail.”

Jawwwn

27,054 views • 7 months ago

Andrej Karpathy: "The future is not writing code. The future is writing the loops that write the code. The leverage point has moved one floor up." This is the shift that defines the next era of coding and quant building. 1 hour of pure insight on agentic AI coding and loop engineering from the OpenAI co-founder who just joined Anthropic to build self improving Claude. Worth more than every $500 vibe coding course combined. Watch it, then read the full guide on how to build your first self improving quant trading loop below.
1:04:50

Andrej Karpathy: "The future is not writing code. The future is writing the loops that write the code. The leverage point has moved one floor up." This is the shift that defines the next era of coding and quant building. 1 hour of pure insight on agentic AI coding and loop engineering from the OpenAI co-founder who just joined Anthropic to build self improving Claude. Worth more than every $500 vibe coding course combined. Watch it, then read the full guide on how to build your first self improving quant trading loop below.

Roan

38,070 views • 3 months ago

The ultimate form of agentic coding will not be the terminal. This is an experiment. An ADE (Agentic Coding Environment), designed from the ground up to work with agentic coding tools. Right now, this only supports Claude Code and runs on a Mac.
5:46

The ultimate form of agentic coding will not be the terminal. This is an experiment. An ADE (Agentic Coding Environment), designed from the ground up to work with agentic coding tools. Right now, this only supports Claude Code and runs on a Mac.

Santiago

40,179 views • 7 months ago

What if Otto (and other agents) could run predictive analysis on any coin/token? Here’s a quick screen video of our devs experimenting during the holidays with a predictive analysis tool (NFA) — powered by AI Agents like Otto, created via Hybrid's agent launcher.
0:44

What if Otto (and other agents) could run predictive analysis on any coin/token? Here’s a quick screen video of our devs experimenting during the holidays with a predictive analysis tool (NFA) — powered by AI Agents like Otto, created via Hybrid's agent launcher.

Hybrid

38,282 views • 1 year ago

A conversation with Claude Code's creator Boris Cherny on the future of agentic coding, the evolution of coding models, and how he uses Claude Code to build Claude Code.
20:20

A conversation with Claude Code's creator Boris Cherny on the future of agentic coding, the evolution of coding models, and how he uses Claude Code to build Claude Code.

Alex Albert

81,455 views • 1 year ago

AI for prod is going to be as big as AI for coding. I sat down with Saam and Sebastian on the heels of our Series A to talk about why. Full video in 🧵
1:07

AI for prod is going to be as big as AI for coding. I sat down with Saam and Sebastian on the heels of our Series A to talk about why. Full video in 🧵

Spiros Xanthos

297,825 views • 7 months ago

Governance is about to change, forever. MetaDAO is unleashing a Solana-native platform that allows DAOs to harness the predictive power of efficient markets. Notifications on.
3:22

Governance is about to change, forever. MetaDAO is unleashing a Solana-native platform that allows DAOs to harness the predictive power of efficient markets. Notifications on.

MetaDAO

94,751 views • 2 years ago