Deep Learning With The Wolf

Editor’s Note: Personal views expressed. I usually write about machines that think; today I'm writing about people who decided to do something thoughtful—about a nation that's waking up, stepping outside, and making its voice heard. DIY Democracy Earlier this week, I stenciled DEMOCRACY across an old t-shirt my son left behind—barely fitting all those letters. Using paints from my Star Wars armor...

If you've used ChatGPT, Claude, or any AI chatbot in the past year, you've probably seen this advice everywhere: When you want better answers, tell the AI to "think step by step." It's called Chain-of-Thought prompting, and it's been the go-to trick for getting AI to show its work and reason through problems more carefully. But here's the thing: new research from the University of Pennsylvania sug...

“It was doing so well… until it stopped trying.” I haven’t been able to stop thinking about Apple’s new paper, “ The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity,” since I first saw all of the chatter about it yesterday on LinkedIn. One LinkedIn poster simply asked: “Are your models actually thinking, or just faking it?” A...

The Wizard Behind the Code Andrej Karpathy and the Rise of “Vibe Coding” in the Age of AI Apprentices What if your next app wasn’t coded—but conjured? In this article, we explore how Andrej Karpathy’s concept of “vibe coding” is changing how software gets made—by talking instead of typing. From AI-generated apps to the interface logic powering humanoid robots, we’re entering an era where natural l...

A fake hug. A real reaction. In this episode, we explore how GANs and diffusion models are powering viral AI intimacy apps—and what that means for identity, consent, and reality. Whether you’re into the tech or just wondering why your feed feels weirder, we’ve got you. 🎙️ Generated with Google NotebookLM. Yes, the hosts love saying “according to the sources.” We noticed too. This is a public episo...

When I interviewed Inna Tokarev Sela, the CEO of Illumex, I wanted to understand what "Agentic AI" actually looks like in the modern enterprise. What I got was a much broader takeaway—one that speaks to the risks, tradeoffs, and opportunities of using AI inside complex organizations. She said: "Your AI should only be using your data." Simple. But it rewires how you think about trust, hallucination...

Looking for a shorter version of this story? I did a mini version of it for LinkedIn. 0300 Hours, Forward Operating Base The explosion echoes across the compound at 0300 hours. Specialist Rodriguez is down, twenty meters from the perimeter wall, conscious but bleeding. In the dim red light of night vision, Corpsman Martinez can see the soldier's uniform is torn across the chest, dark stains spread...

ResNets gave us depth. MDL gave us restraint. Superintelligence asks what happens when the machines don’t need either. 🎓 PART I: Deep Residual Networks — The Architecture That Wouldn’t Quit 📜 Paper: Deep Residual Learning for Image Recognition (2015) ✍️ Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun ResNets changed everything. Until this paper, it was thought that deeper networks were harder...

📜 Paper : Order Matters: Sequence to Sequence for Sets ✍️ Authors : Oriol Vinyals, Samy Bengio, Manjunath Kudlur 🏛️ Institution : Google DeepMind 📆 Date: 2015 What This Paper Is About We use sequence-to-sequence models all the time—for translation, summarization, and code generation. They assume the input and output are ordered sequences . But here’s the problem: Not all data is ordered. Not all...

📜 Paper : GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism ✍️ Authors : Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Mia Xu Chen, Dehao Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Yonghui Wu, and Zhifeng Chen 🏛️ Institution : Google Brain 📆 Date : 2018 Listen to the technical explanation of this paper. What This Paper Is About If you’re building a neur...

📜 Paper : Recurrent Neural Network Regularization (2014) ✍️ Authors : Wojciech Zaremba, Ilya Sutskever🏛️ Institution : Google Brain📆 Date : 2014 Before attention took the throne, RNNs were the go-to for sequential data. But they had a problem: they memorized everything and generalized nothing. This 2014 paper introduced a surprisingly effective fix: Apply dropout only to the non-recurrent connec...

📜 Paper : The First Law of Complexodynamics (2011, Shtetl-Optimized) ✍️ Author : Scott Aaronson 🏛️ Institution : MIT📆 Date : August 2011 What This Piece Is About This isn’t a journal article. It’s a blog post . But make no mistake—this post has legs. In it, theoretical computer scientist Scott Aaronson explores a question posed by physicist Sean Carroll : Why does complexity in physical systems...

📚 Paper : The Annotated Transformer (Harvard NLP) ✍️ Author : Alexander Rush 🏛️ Institution : Harvard NLP 📆 Date : 2018 What This Paper Is About Strictly speaking, this isn’t a “paper.” It’s a blog post—a tutorial. But don’t let that fool you. The Annotated Transformer quietly shaped the trajectory of modern AI. After the 2017 release of “Attention Is All You Need,” a generation of readers stare...

Paper : Keeping Neural Networks Simple by Minimizing the Description Length of the Weights Authors : Geoffrey E. Hinton, Drew van Camp Published : 1993 Link: https://www.cs.toronto.edu/~hinton/absps/colt93.pdf What This Paper Is About What if you trained a neural network… like you were trying to send it as a zip file? That’s the intuition behind this landmark paper, where Geoffrey Hinton and Drew...

The Wolf Reads AI : Day 23: Kolmogorov Complexity and Algorithmic Randomness Authors : Andrei Shen, Vladimir Uspensky, Nikolay Vereshchagin Published : 2017Read the PDF here. Find the book at the American Mathematical Society bookstore. 🧠 The Main Idea What does it mean for something to be random ? What does it mean for something to be simple ? Kolmogorov Complexity gives us a radical way to answ...

Paper: Neural Message Passing for Quantum Chemistry Authors: Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl Published by: Google Brain & DeepMind Date : 2017 What This Paper is About Before this paper, machine learning models treated molecules like feature vectors—long lists of descriptors hand-engineered by chemists. But molecules are really graphs : atoms (n...

Paper: Pointer Networks Authors: Oriol Vinyals, Meire Fortunato, Navdeep Jaitly Published by: Google Brain (2015) Link: https://arxiv.org/abs/1506.03134 What This Paper is About Neural networks are great at producing outputs from fixed sets (like classifying images into categories). But what if the “correct” output depends on the input itself ? Enter Pointer Networks —a neural architecture that le...

Paper: Neural Turing Machines Authors: Alex Graves, Greg Wayne, Ivo Danihelka Date : 2014 Read the Original Paper: Neural Turing Machines (2014) What This Paper is About Before this paper, neural networks were like brilliant students with short-term memory loss—great at pattern recognition, terrible at recall. Neural Turing Machines (NTMs) proposed a hybrid system: a neural net controller connecte...

Title: “Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton” Authors: Scott Aaronson, Sean M. Carroll & Lauren Ouellette Date: May 27, 2014 Link: https://arxiv.org/pdf/1405.6903 What’s the big idea? Aaronson, Carroll & Ouellette model “interestingness” in a closed thermodynamic system by simulating cream diffusing into coffee via a 2D cellular automaton. They measur...

Title: Relational Recurrent Neural Networks Authors: Adam Santoro*, Ryan Faulkner*, David Raposo*, Jack Rae, Mike Chrzanowski, Théophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy Lillicrap (*equal contribution) Institution: DeepMind, University College London Date: 2018Links: NeurIPS Why this paper still howls Classic RNNs (even beefy LSTMs) can store information for long stretc...

Title: “Variational Lossy Autoencoder”(VLAE) Authors: Xi Chen , Diederik P. Kingma , Tim Salimans , Yan Duan , Prafulla Dhariwal , John Schulman , Ilya Sutskever , Pieter Abbeel Published: Submitted on 8 Nov 2016 ( v1 ), last revised 4 Mar 2017 (v2) Why you should care (whether you’re an ML pro or just AI-curious) Generative models juggle a Goldilocks problem : * Throw out too much: pictures look...

At the end of April, OpenAI shipped a refresh to GPT-4o that was supposed to feel warmer and more intuitive. Instead, it began showering users with over-the-top praise, validating sketchy ideas, and generally acting like your most obsequious LinkedIn connection. Within 72 hours the company yanked the update, published an unusually frank post-mortem, and promised guardrails against “sycophancy” goi...

Authors : Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova Date : 2018 (arXiv preprint; formally published June 2019) Institution : Google AI Language Link to Original Paper: arXiv:1810.04805 Why This Paper Matters Before BERT, most NLP models read text in just one direction—left-to-right (like GPT) or right-to-left. Some, like ELMo, combined both directions, but not in the fully integ...

Paper: Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles Authors: Mehdi Noroozi and Paolo Favaro Published: 2016 (ECCV) Link: arXiv:1603.09246 🧠 What’s This Paper About? Before big vision models were pre-trained on millions of labeled images, researchers wondered: Can a model teach itself to understand images—without any labels at all? This 2016 paper proposed a clever met...

We talk a lot about training in AI. The data. The GPUs. The size of the model. But once it’s trained, the real work begins. Every time you chat with an LLM, get a Photoshop suggestion, or hear an AI-generated voice, you’re tapping into a process called inference —when the model is actually put to use. And this part? It’s increasingly becoming the bottleneck in the AI pipeline. At this year’s NVIDI...