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Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter AI-Powered Feature Engineering with n8n: Scaling Data Science Intelligence Generate strategic feature engineering recommendations using AI-powered workflows in n8n.
About Song Han News Publications Blog Course Awards Talks Media Team Gallery Efficient AI Computing, Transforming the Future. How Attention Sinks Keep Language Models Stable Guangxuan Xiao August 7, 2025 TL;DR We discovered why language models catastrophically fail on long conversations: when old tokens are removed to save memory, models produce complete gibberish.
The hierarchical reasoning model is revolutionizing how artificial intelligence (AI) systems approach complex problem-solving. At the very beginning of this post, let’s clarify: the hierarchical reasoning model is a brain-inspired architecture that enables AI to break down and solve intricate tasks by leveraging multi-level reasoning, adaptive computation, and deep latent processing.
One of the fastest-growing areas of technology is machine learning, but even seasoned professionals occasionally stumble over new terms and jargon. It is simple to get overwhelmed by the plethora of technical terms as research speeds up and new architectures, loss functions, and optimisation techniques appear. This blog article is your carefully chosen reference to […] The post 50+ Must-Know Machine Learning Terms You (Probably) Haven’t Heard Of appeared first on Analytics Vidhya.
Speaker: Jason Chester, Director, Product Management
In today’s manufacturing landscape, staying competitive means moving beyond reactive quality checks and toward real-time, data-driven process control. But what does true manufacturing process optimization look like—and why is it more urgent now than ever? Join Jason Chester in this new, thought-provoking session on how modern manufacturers are rethinking quality operations from the ground up.
Microsoft is integrating OpenAI’s open-weight language models, gpt-oss, into Azure AI Foundry and Windows AI Foundry, broadening its AI toolset. This expansion introduces gpt-oss-120b and gpt-oss-20b models to personal computers. The gpt-oss-120b model is designed for high-performance reasoning applications. Conversely, the gpt-oss-20b model operates on personal computers equipped with graphics processing units possessing a minimum of 16 gigabytes of memory.
Summary: Adopting DataOps transforms data science practices by automating workflows, ensuring higher data quality, and fostering collaboration among teams. This approach enhances efficiency, scales operations easily, and proactively reduces risks through early error detection and robust governance. Organizations benefit from accelerated insights, improved reliability, and optimized use of data resources.
Discover a comprehensive collection of cheat sheets covering Docker commands, mathematics, Python, machine learning, data science, data visualization, CLI commands, and more.
Discover a comprehensive collection of cheat sheets covering Docker commands, mathematics, Python, machine learning, data science, data visualization, CLI commands, and more.
Jump to Content Research Research Who we are Back to Who we are menu Defining the technology of today and tomorrow. Philosophy We strive to create an environment conducive to many different types of research across many different time scales and levels of risk. Learn more about our Philosophy Learn more Philosophy People Our researchers drive advancements in computer science through both fundamental and applied research.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter 10 Python Libraries Every MLOps Engineer Should Know Learn about 10 essential Python libraries that support core MLOps tasks like versioning, deployment, and monitoring.
Skip to main content Login Why Databricks Discover For Executives For Startups Lakehouse Architecture Mosaic Research Customers Customer Stories Partners Cloud Providers Databricks on AWS, Azure, GCP, and SAP Consulting & System Integrators Experts to build, deploy and migrate to Databricks Technology Partners Connect your existing tools to your Lakehouse C&SI Partner Program Build, deploy or migrate to the Lakehouse Data Partners Access the ecosystem of data consumers Partner Solutions
Graph rag is rapidly emerging as the gold standard for context-aware AI, transforming how large language models (LLMs) interact with knowledge. In this comprehensive guide, we’ll explore the technical foundations, architectures, use cases, and best practices of graph rag versus traditional RAG, helping you understand which approach is best for your enterprise AI, research, or product development needs.
ETL and ELT are some of the most common data engineering use cases, but can come with challenges like scaling, connectivity to other systems, and dynamically adapting to changing data sources. Airflow is specifically designed for moving and transforming data in ETL/ELT pipelines, and new features in Airflow 3.0 like assets, backfills, and event-driven scheduling make orchestrating ETL/ELT pipelines easier than ever!
This article introduces and discusses four key reasons why data visualization is essential in data storytelling: simplifying complex information, discovering hidden patterns, fostering engagement and impact, and supporting informed decisions.
A bit, or binary digit, serves as the cornerstone of digital technology, representing the basic elements that form every piece of data within a computer. Understanding bits allows us to grasp how vast volumes of information are processed and stored. From simple representation of numbers to complex operations in encryption, bits play an indispensable role in various computing fields.
Skip to Content MIT Technology Review Featured Topics Newsletters Events Audio Sign in Subscribe MIT Technology Review Featured Topics Newsletters Events Audio Sign in Subscribe Artificial intelligence Five ways that AI is learning to improve itself From coding to hardware, LLMs are speeding up research progress in artificial intelligence. It could be the most important trend in AI today.
Skip to main content Login Why Databricks Discover For Executives For Startups Lakehouse Architecture Mosaic Research Customers Customer Stories Partners Cloud Providers Databricks on AWS, Azure, GCP, and SAP Consulting & System Integrators Experts to build, deploy and migrate to Databricks Technology Partners Connect your existing tools to your Lakehouse C&SI Partner Program Build, deploy or migrate to the Lakehouse Data Partners Access the ecosystem of data consumers Partner Solutions
Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.
GPT OSS is OpenAI’ s latest leap in democratizing artificial intelligence, offering open-weigh t large language models (LLMs) that anyone can download, run, and fine-tune on their own hardware. Unlike proprietary models locked behind APIs, gpt oss models — gpt-oss-120b and gpt-oss-20b —are designed for transparency, customization, and local inference, marking a pivotal shift in the AI landscape.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter Getting The Most From The LangChain Ecosystem Learn how to use the LangChain ecosystem to build, test, deploy, monitor, and visualize complex agentic workflows.
In time series analysis and forecasting , transforming data is often necessary to uncover underlying patterns, stabilize properties like variance, and improve the performance of predictive models.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter 5 Routine Tasks That ChatGPT Can Handle for Data Scientists A practical walkthrough of how ChatGPT handles cleaning, exploration, visualization, modeling and more.
Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.
OpenAI has released GPT-OSS 12B and 20B, open-source AI models that enable local operation on personal computers. This development offers enhanced privacy and control. OpenAI’s GPT-OSS 12B and GPT-OSS 20B are reasoning models. They facilitate advanced AI capabilities on local systems, enhancing privacy, speed, and user control. GPT-OSS 20B is optimized for high-end consumer hardware, while GPT-OSS 12B is for professional-grade systems with more powerful GPUs.
Device-directed speech detection (DDSD) is a binary classification task that separates the user’s queries to a voice assistant (VA) from background speech or side conversations. This is important for achieving naturalistic user experience. To this end, we propose knowledge distillation (KD) to enhance DDSD accuracy while ensuring efficient deployment.
We introduce a modern Hopfield network with continuous states and a corresponding update rule. The new Hopfield network can store exponentially (with the dimension of the associative space) many patterns, retrieves the pattern with one update, and has exponentially small retrieval errors. It has three types of energy minima (fixed points of the update): (1) global fixed point averaging over all patterns, (2) metastable states averaging over a subset of patterns, and (3) fixed points which store
In the world of data engineering, the most impactful work is often the least glamorous. At ODSC East, Veronika Durgin, VP of Data at Saks, struck a chord with her talk on the “10 Most Neglected Data Engineering Tasks.” Drawing from decades of experience in data architecture, engineering, and analytics, she emphasized the foundational practices that keep pipelines stable, teams agile, and businesses prepared for rapid technological change.
In Airflow, DAGs (your data pipelines) support nearly every use case. As these workflows grow in complexity and scale, efficiently identifying and resolving issues becomes a critical skill for every data engineer. This is a comprehensive guide with best practices and examples to debugging Airflow DAGs. You’ll learn how to: Create a standardized process for debugging to quickly diagnose errors in your DAGs Identify common issues with DAGs, tasks, and connections Distinguish between Airflow-relate
Publish AI, ML & data-science insights to a global community of data professionals. Sign in Sign out Submit an Article Latest Editor’s Picks Deep Dives Newsletter Write For TDS Toggle Mobile Navigation LinkedIn X Toggle Search Search Artificial Intelligence How a Research Lab Made Entirely of LLM Agents Developed Molecules That Can Block a Virus Welcome to the 21st century by the hand of large language models and reasoning AI agents Luciano Abriata Aug 5, 2025 10 min read Share A human user
Nvidia released a software update on Saturday to address critical vulnerabilities in its Triton server, identified by cybersecurity firm Wiz , which could enable AI model takeover, data theft, and response manipulation. The vulnerabilities, deemed “critical” by Wiz, pertain to Nvidia’s Triton server, employed by clients to execute artificial intelligence models.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter The Case for Makefiles in Python Projects (And How to Get Started) Most Python projects rely on scattered scripts and commands.
Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage
There’s a lot of noise surrounding the ability of AI agents to connect to your tools, systems and data. But building an AI application into a reliable, secure workflow agent isn’t as simple as plugging in an API. As an engineering leader, it can be challenging to make sense of this evolving landscape, but agent tooling provides such high value that it’s critical we figure out how to move forward.
Massive data centers have become the backbone of the digital economy and the AI revolution. These facilities — housing tens of thousands of servers in warehouse-sized buildings — enable everything from cloud services to training advanced AI models. In parallel, private money from tech corporations and venture capital is pouring into new AI infrastructure (like supercomputing clusters and specialized chips) at unprecedented levels.
The rapid advancement of key technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and edge-cloud computing has significantly accelerated the transformation toward smart industries across various domains, including finance, manufacturing, and healthcare. Edge and cloud computing offer low-cost, scalable, and on-demand computational resources, enabling service providers to deliver intelligent data analytics and real-time insights to end-users.
Microsoft has developed Project Ire, an AI prototype that can autonomously reverse engineer software to identify malware, a task typically performed by human security researchers. The prototype can fully reverse engineer software without prior clues about its origin or purpose. In a Microsoft test, Project Ire accurately identified 90% of malicious Windows driver files , flagging only 2% of benign files as dangerous.
This entry is a part of our Meet the Fellow blog series, which introduces and highlights incoming Faculty Fellows at CDS. Meet CDS Faculty Fellow Nicholas Tomlin , who joined us earlier this summer. Tomlin recently completed his PhD at Berkeley EECS, where he was advised by Dan Klein and affiliated with Berkeley NLP and BAIR. In 2026, he will take on a position at TTIC as an Assistant Professor.
Speaker: Andrew Skoog, Founder of MachinistX & President of Hexis Representatives
Manufacturing is evolving, and the right technology can empower—not replace—your workforce. Smart automation and AI-driven software are revolutionizing decision-making, optimizing processes, and improving efficiency. But how do you implement these tools with confidence and ensure they complement human expertise rather than override it? Join industry expert Andrew Skoog as he explores how manufacturers can leverage automation to enhance operations, streamline workflows, and make smarter, data-dri
Explore PaliGemma 2, which offers scalable performance with multiple model sizes and resolutions, and is designed as a drop-in replacement for existing PaliGemma users.
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