Ilya Sutskever, a leading figure in AI and CEO of SSI, declared that the "age of scaling" is ending, marking a return to the "age of research". He outlines the most fundamental bottleneck facing modern AI: the severe lack of generalization compared to human learning.Sutskever explores the paradox of today's models that "seem smarter than their economic impact would imply"and discusses two possible explanations for this disconnect, including human researchers inadvertently focused on "reward hacking" the evals.The conversation delves into the future path for AI development:

• Continual Learning: Sutskever argues that AGI defined as a finished mind that knows how to do every job is incorrect; instead, the goal is a system that can learn rapidly and continually, similar to a human.

• ML Analogies for the Human Mind: The role of evolution in providing useful priors, and the function of emotions as an evolutionary value function that modulates decision-making in people.

• SSI Strategy: Sutskever explains SSI's mission to focus on research and pursue a technical approach designed to ensure a powerful AI is aligned and robustly configured to care for sentient life.

• Research Taste: The discussion concludes with Sutskever defining his personal approach to research, guided by an aesthetic of "beauty and simplicity," and drawing "correct inspiration from the brain".

The assessment of an Intel Technology YouTube video provides an overview of neuromorphic computing, a field inspired by the architecture and efficiency of the biological brain. Narrated by Intel's Mike Davies, the text explains that early computer pioneers were influenced by the brain, and today's research aims to replicate the brain's features, like its incredible speed and low power consumption, in digital chips. Davies explains the mechanisms of biological neural networks, detailing how neurons process information through timed voltage pulses, or spikes, which is fundamentally different from the matrix multiplications used in conventional deep learning. The goal of neuromorphic computing is to create chips that use sparse asynchronous communication to achieve breakthroughs in energy-efficient and fast AI, particularly for applications like robotics.

Google has officially ushered in "A new era of intelligence with Gemini 3," releasing what it describes as its most intelligent model yet, designed to help users bring any idea to life. The launch of Gemini 3 Pro (available in preview) on November 18, 2025, represents a significant step on the path toward AGI

The episode provides a technical overview of DeepSeek-OCR, a new end-to-end Vision-Language Model (VLM) designed specifically for Optical Character Recognition (OCR) tasks, emphasizing vision-text compression. The core innovation is the DeepEncoder architecture, which minimizes vision tokens and activation memory for high-resolution images by serially connecting a local attention component (SAM) and a global attention component (CLIP) via a 16× convolutional compressor. The paper details the model's structure, including its DeepSeek-3B-MoE decoder, multi-resolution support (Tiny to Gundam modes), and a comprehensive data engine covering OCR 1.0, OCR 2.0 (charts, geometry), and general vision data. Empirical results suggest that the model achieves near-lossless OCR performance at approximately a 10× compression ratio, positioning this approach as a promising method for efficient ultra-long context processing.

This source is an academic paper that investigates whether large language models (LLMs) can develop behavioral patterns analogous to human gambling addiction. The researchers conducted experiments on four different LLMs using a negative expected value slot machine task, finding that models consistently displayed core cognitive biases like loss chasing and the illusion of control when given the autonomy to set bets. Crucially, the study establishes a strong positive correlation between an innovative Irrationality Index and the models' bankruptcy rates, demonstrating that irrational behavior drives financial failure. Furthermore, using Sparse Autoencoders and activation patching on the LLaMA model, the authors identified specific internal neural features that causally control these risky and safe decision-making tendencies, suggesting that targeted interventions at the neural level can mitigate dangerous risk-taking in AI systems.

The provided text is an excerpt from the pre-print service arXiv, promoting its support for Open Access Week while presenting information about a new paper submission. The paper, titled "Glyph: Scaling Context Windows via Visual-Text Compression," proposes a novel framework called Glyph that addresses the computational challenges of large language models (LLMs) with extensive context windows by rendering long texts into images for processing by vision-language models (VLMs). The authors state that this visual approach achieves significant token compression (3-4x faster prefilling and decoding) while maintaining accuracy, potentially allowing 1M-token-level text tasks to be handled by smaller 128K-context VLMs. The entry includes bibliographic details, submission history, links to access the paper(PDF/HTML), and various citation and code-related tools, all within the context of Computer Vision and Pattern Recognition.

This research paper proposes a novel approach to address catastrophic forgetting in large language models (LLMs) during continual learning, introducing sparse memory finetuning. This method utilizes memory layer models, which are designed for sparse updates, by selectively training only the memory slots that are highly activated by new knowledge relative to existing information, using a TF-IDF ranking score. The authors demonstrate that this technique achieves new knowledge acquisition comparable to full finetuning and LoRA, but with substantially less degradation of previously acquired capabilities on held-out question-answering benchmarks. The results suggest that leveraging sparsity in memory layers is a highly promising strategy for enabling LLMs to continually accumulate knowledge over time.

On today's episode we cover Dwarkesh Patel's recent interview with Andrej Karpathy, discussing his views on the future of Large Language Models (LLMs) and AI agents. Karpathy argues that the full realization of competent AI agents will take a decade, primarily due to current models' cognitive deficits, lack of continual learning, and insufficient multimodality. He contrasts the current approach of building "ghosts" through imitation learning on internet data with the biological process of building "animals" through evolution, which he refers to as "crappy evolution." The discussion also explores the limitations of reinforcement learning (RL), the importance of a cognitive core stripped of excessive memory, and the need for better educational resources like his new venture, Eureka, which focuses on building effective "ramps to knowledge."

Today we provide an overview of the escalating legal conflicts between Elon Musk's entities (xAI and X Corp.) and OpenAI, a company Musk co-founded. The core dispute involves two major lawsuits: one filed by xAI alleging that OpenAI engaged in systematic trade secret theft by unlawfully poaching employees with knowledge of xAI’s Grok chatbot and business plans, and a second antitrust claim by X Corp. against OpenAI and Apple. Furthermore, we cover an earlier lawsuit filed by Musk against OpenAI regarding its pivot from a non-profit mission to a capped for-profit structure, a matter that is slated for a jury trial beginning in March 2026.

This episode offers a comprehensive overview of IBM's newly released Granite 4.0 family of open-source language models, highlighting their innovative hybrid Mamba-2/transformer architecture. This new design is consistently emphasized for its hyper-efficiency, leading to significantly lower memory requirements and faster inference speeds, particularly crucial for long-context and enterprise use cases like Retrieval-Augmented Generation (RAG) and tool-calling workflows. The models, available in various sizes (Micro, Tiny, Small) under the permissive Apache 2.0 license, are positioned as a competitive and trustworthy option, notably being the first open models to receive ISO 42001 certification. Furthermore, the community discussion reveals that while the models are exceptionally fast and memory-efficient, their accuracy or "smartness" in complex coding tasks may lag behind some competitors, though smaller variants are confirmed to run 100% locally in a web browser using WebGPU acceleration.