Finite-Time Analysis of Gradient Descent for Shallow Transformerscs.AI updates on arXiv.org arXiv:2601.16514v1 Announce Type: cross
Abstract: Understanding why Transformers perform so well remains challenging due to their non-convex optimization landscape. In this work, we analyze a shallow Transformer with $m$ independent heads trained by projected gradient descent in the kernel regime. Our analysis reveals two main findings: (i) the width required for nonasymptotic guarantees scales only logarithmically with the sample size $n$, and (ii) the optimization error is independent of the sequence length $T$. This contrasts sharply with recurrent architectures, where the optimization error can grow exponentially with $T$. The trade-off is memory: to keep the full context, the Transformer’s memory requirement grows with the sequence length. We validate our theoretical results numerically in a teacher-student setting and confirm the predicted scaling laws for Transformers.
arXiv:2601.16514v1 Announce Type: cross
Abstract: Understanding why Transformers perform so well remains challenging due to their non-convex optimization landscape. In this work, we analyze a shallow Transformer with $m$ independent heads trained by projected gradient descent in the kernel regime. Our analysis reveals two main findings: (i) the width required for nonasymptotic guarantees scales only logarithmically with the sample size $n$, and (ii) the optimization error is independent of the sequence length $T$. This contrasts sharply with recurrent architectures, where the optimization error can grow exponentially with $T$. The trade-off is memory: to keep the full context, the Transformer’s memory requirement grows with the sequence length. We validate our theoretical results numerically in a teacher-student setting and confirm the predicted scaling laws for Transformers. Read More
What is Clawdbot? How a Local First Agent Stack Turns Chats into Real AutomationsMarkTechPost Clawdbot is an open source personal AI assistant that you run on your own hardware. It connects large language models from providers such as Anthropic and OpenAI to real tools such as messaging apps, files, shell, browser and smart home devices, while keeping the orchestration layer under your control. The interesting part is not that
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Clawdbot is an open source personal AI assistant that you run on your own hardware. It connects large language models from providers such as Anthropic and OpenAI to real tools such as messaging apps, files, shell, browser and smart home devices, while keeping the orchestration layer under your control. The interesting part is not that
The post What is Clawdbot? How a Local First Agent Stack Turns Chats into Real Automations appeared first on MarkTechPost. Read More
Researchers tested AI against 100,000 humans on creativityArtificial Intelligence News — ScienceDaily A massive new study comparing more than 100,000 people with today’s most advanced AI systems delivers a surprising result: generative AI can now beat the average human on certain creativity tests. Models like GPT-4 showed strong performance on tasks designed to measure original thinking and idea generation, sometimes outperforming typical human responses. But there’s a clear ceiling. The most creative humans — especially the top 10% — still leave AI well behind, particularly on richer creative work like poetry and storytelling.
A massive new study comparing more than 100,000 people with today’s most advanced AI systems delivers a surprising result: generative AI can now beat the average human on certain creativity tests. Models like GPT-4 showed strong performance on tasks designed to measure original thinking and idea generation, sometimes outperforming typical human responses. But there’s a clear ceiling. The most creative humans — especially the top 10% — still leave AI well behind, particularly on richer creative work like poetry and storytelling. Read More
SAM 3 vs. Specialist Models — A Performance BenchmarkTowards Data Science Why specialized models still hold the 30x speed advantage in production environments
The post SAM 3 vs. Specialist Models — A Performance Benchmark appeared first on Towards Data Science.
Why specialized models still hold the 30x speed advantage in production environments
The post SAM 3 vs. Specialist Models — A Performance Benchmark appeared first on Towards Data Science. Read More
Azure ML vs. AWS SageMaker: A Deep Dive into Model Training — Part 1Towards Data Science Compare Azure ML and AWS SageMaker for scalable model training, focusing on project setup, permission management, and data storage patterns, to align platform choices with existing cloud ecosystem and preferred MLOps workflows
The post Azure ML vs. AWS SageMaker: A Deep Dive into Model Training — Part 1 appeared first on Towards Data Science.
Compare Azure ML and AWS SageMaker for scalable model training, focusing on project setup, permission management, and data storage patterns, to align platform choices with existing cloud ecosystem and preferred MLOps workflows
The post Azure ML vs. AWS SageMaker: A Deep Dive into Model Training — Part 1 appeared first on Towards Data Science. Read More
Author: Derrick D. JacksonTitle: Founder & Senior Director of Cloud Security Architecture & RiskCredentials: CISSP, CRISC, CCSPLast updated January 24th, 2026 What Is ISO 42001 Clause 10: Improvement? The Final Phase of AI Governance That Actually Matters You’ve built your AI management system. Policies are documented. Risk assessments are complete. Audits have happened. Now what? This […]
How to Build a Neural Machine Translation System for a Low-Resource LanguageTowards Data Science An introduction to neural machine translation
The post How to Build a Neural Machine Translation System for a Low-Resource Language appeared first on Towards Data Science.
An introduction to neural machine translation
The post How to Build a Neural Machine Translation System for a Low-Resource Language appeared first on Towards Data Science. Read More
Air for Tomorrow: Mapping the Digital Air-Quality Landscape, from Repositories and Data Types to Starter CodeTowards Data Science Understand air quality: access the available data, interpret data types, and execute starter codes
The post Air for Tomorrow: Mapping the Digital Air-Quality Landscape, from Repositories and Data Types to Starter Code appeared first on Towards Data Science.
Understand air quality: access the available data, interpret data types, and execute starter codes
The post Air for Tomorrow: Mapping the Digital Air-Quality Landscape, from Repositories and Data Types to Starter Code appeared first on Towards Data Science. Read More
GitHub Releases Copilot-SDK to Embed Its Agentic Runtime in Any AppMarkTechPost GitHub has opened up the internal agent runtime that powers GitHub Copilot CLI and exposed it as a programmable SDK. The GitHub Copilot-SDK, now in technical preview, lets you embed the same agentic execution loop into any application so the agent can plan, invoke tools, edit files, and run commands as part of your own
The post GitHub Releases Copilot-SDK to Embed Its Agentic Runtime in Any App appeared first on MarkTechPost.
GitHub has opened up the internal agent runtime that powers GitHub Copilot CLI and exposed it as a programmable SDK. The GitHub Copilot-SDK, now in technical preview, lets you embed the same agentic execution loop into any application so the agent can plan, invoke tools, edit files, and run commands as part of your own
The post GitHub Releases Copilot-SDK to Embed Its Agentic Runtime in Any App appeared first on MarkTechPost. Read More
How Machine Learning and Semantic Embeddings Reorder CVE Vulnerabilities Beyond Raw CVSS ScoresMarkTechPost In this tutorial, we build an AI-assisted vulnerability scanner that goes beyond static CVSS scoring and instead learns to prioritize vulnerabilities using semantic understanding and machine learning. We treat vulnerability descriptions as rich linguistic artifacts, embed them using modern sentence transformers, and combine these representations with structural metadata to produce a data-driven priority score. Also,
The post How Machine Learning and Semantic Embeddings Reorder CVE Vulnerabilities Beyond Raw CVSS Scores appeared first on MarkTechPost.
In this tutorial, we build an AI-assisted vulnerability scanner that goes beyond static CVSS scoring and instead learns to prioritize vulnerabilities using semantic understanding and machine learning. We treat vulnerability descriptions as rich linguistic artifacts, embed them using modern sentence transformers, and combine these representations with structural metadata to produce a data-driven priority score. Also,
The post How Machine Learning and Semantic Embeddings Reorder CVE Vulnerabilities Beyond Raw CVSS Scores appeared first on MarkTechPost. Read More