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The primary focus of this blogpost and the corresponding YouTube is on LeCun’s architecture, centered on the (Hierarchical) Joint Embedding Predictive Architecture (JEPA, or H-JEPA). This will be foundational to a contrast-and-compare with Action Percept Divergence (APD) (Hafner et al., 2022), which we’ll evolve over this Summer, 2024 YouTube/blogpost series. This blogpost accompanies a YouTube… Continue reading Comparing Three Leading AGI Contenders (Part 1 of Many)

It’s more than just legislation – although a “generative AI labeling” bill is in committee in both the U.S. Senate and the House. California has just passed a first effort at legislating extreme AI behaviors, and Texas (another bellwether state) also has AI legislation in committee. So let’s start with a quick recap of WHY… Continue reading AI Legislation, Safety, and Mis-Use Cases

There are three fundamentally different forms of AI:  Most of the attention now is on transformer-based methods, which are a particular form of generative AI. Generative AI comes in several forms, of which transformers are the most recent. 

We’re building our AGI discussion around a few core papers. The ones identified here are either central to our discussion – and will be featured across multiple YouTubes and blogposts (and also front-row in our AGI 101 Themesis course), or are hugely and supremely supportive of our studies. This blogpost specifically correlates with THIS YOUTUBE:… Continue reading AGI Basics: Five Key Reads

This is a blogpost-in-progress. It is intended to provide resources accompanying a YouTube that I plan to put out this week. Check back for more as the week progresses – thank you! – AJM (Monday, May 6th, 2024; 1AM Hawai’i time) Resources and References From the Prologue to LeCun’s work: This document is not a… Continue reading AGI: Fundamental Concepts (Getting Started Reading List)

Four years ago, tech journalist and old friend Lee Goldberg asked me to semi-/sort-of collaborate with him on his yearly April Fool’s (April 1st) day article. Basically, Lee had a really fun idea for writing about “artificial stupidity,” instead of “artificial intelligence,” and he asked me to give him some real-AI credibility to use in… Continue reading Generative AI: A Teenager Acting Out

First, Make Sure that You Understand Discriminative Neural Networks Discriminative neural networks are the kinds of neural networks where you train the network with a known and pre-labeled training and testing data set. Essentially, you “have the answers in the back of the book” – and you train the network by constantly having it check… Continue reading Your Generative AI Self-Study Plan

Teaching yourself generative AI (“gen-AI”) has to be one of the hardest things in the world. The really important, classic papers – the ones that you WISH THAT YOU COULD READ – all presuppose that you have a lot of knowledge coming in, about all SORTS of things. The situation is the same as it… Continue reading If You’re Teaching Yourself Generative AI: Some Resources (Book Chapters)

In order to read any of the classic (and important) papers on energy-based neural networks, we need to know the vocabulary and essential concepts from: In today’s associated YouTube video, we illustrate how these different terms – and their respective disciplines – are blended together, using the Salakhutdinov and Hinton (2012) paper as a reference… Continue reading Learning Energy-Based Neural Networks

Socrates is a name that most of us know, even if only tangentially. We’ve probably all heard of the “Socratic method” of teaching. Many of us also know of Pericles, the great Athenian general who lead their armies in their various wars – including those against Sparta. But Aspasia? Not as many of us have… Continue reading Socrates, Pericles, and Aspasia: How A Muse Helped the Genesis of Western Thought