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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 Build ETL Pipelines for Data Science Workflows in About 30 Lines of Python Want to understand how ETL really works?
FutureHouse, a research lab co-founded by Sam Rodriques PhD ’19 and Andrew White, is developing an AI platform designed to automate many of the most critical steps in the scientific process. The goal is to address a well-documented problem: scientific productivity is declining. Automating science to reverse declining productivity Over the last few decades, researchers have observed that scientific discovery is becoming slower and more resource-intensive.
Forget data silos. You can build a modern data lakehouse that gives you transactional consistency, schema evolution, and top-tier performance, all in one place with Apache Iceberg and Apache Spark.
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 Build Interactive Machine Learning Apps with Gradio Create a fun text-to-speech demo in minutes Ehssan Khan Jul 8, 2025 7 min read Share Image by author As a developer working with machine learning models, you likely spend hours writing s
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.
Are you an AI engineer, wondering how to attain resources that can put your skills to a practical test? It might be difficult to look for the right solution for you, based on the vast amount of information out there. Hence, we present this list of all ten GitHub llm repositories every AI engineer ought […] The post 10 GitHub LLM Repositories Every AI Engineer Should Know appeared first on Analytics Vidhya.
The traditional single-modal data approaches often miss important insights that are present in cross-modal relations. Multi-Modal Analysis brings together diverse sources of data, such as text, images, audio, and more similar data to provide a more complete view of an issue. This multi-modal data analysis is called multi-modal data analytics, and it improves prediction accuracy […] The post What is Multi-Modal Data Analysis?
The traditional single-modal data approaches often miss important insights that are present in cross-modal relations. Multi-Modal Analysis brings together diverse sources of data, such as text, images, audio, and more similar data to provide a more complete view of an issue. This multi-modal data analysis is called multi-modal data analytics, and it improves prediction accuracy […] The post What is Multi-Modal Data Analysis?
Model Context Protocol (MCP) is rapidly emerging as the foundational layer for intelligent, tool-using AI systems, especially as organizations shift from prompt engineering to context engineering. Developed by Anthropic and now adopted by major players like OpenAI and Microsoft , MCP provides a standardized, secure way for large language models (LLMs) and agentic systems to interface with external APIs, databases, applications, and tools.
AI is undeniably powerful, performing tasks that seem superhuman in speed and scope. Yet, for all its capabilities, current models remain fundamentally limited — they can only do what they’re programmed to do. What if AI could think like a human? This question is behind the vision driving researchers toward technology known as the digital brain, which mimics biological neural processes.
CMU researchers are presenting 127 papers at the Forty-Second International Conference on Machine Learning (ICML 2025), held from July 13th-19th at the Vancouver Convention Center. Here is a quick overview of the areas our researchers are working on: Here are our most frequent collaborator institutions: Table of Contents Oral Papers Spotlight Papers Poster Papers Accountability, Transparency, And Interpretability Active Learning And Interactive Learning Applications Causality Chemistry, Physics,
Many businesses struggle to keep up with rapidly changing consumer behaviour. Predictive analytics uses data, statistical algorithms, and machine learning to forecast future trends. This technology has become increasingly relevant across industries — including UK casinos not on GamStop , where understanding player preferences is key to retention and engagement.
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!
Skip to main content Skip to main navigation menu Skip to site footer Open Menu AI Magazine Current Archives Announcements About About the Journal Submissions Editorial Team Privacy Statement Contact Search Search Login Search Search Home / Archives / Vol. 31 No. 3: Fall 2010 / Articles Building Watson: An Overview of the DeepQA Project Authors David Ferrucci IBM T.
PayPal has implemented a new AI-powered scam alert system to enhance fraud prevention for its global user base, including Venmo users in the United States, according to ZDNet. This initiative combats evolving scam tactics by leveraging artificial intelligence to identify and mitigate potential risks in transactions. PayPal’s new system deploys AI-powered scam alerts specifically for Friends and Family transactions on the PayPal platform globally and for Venmo transactions within the U.S.
Author(s): Towards AI Editorial Team Originally published on Towards AI. What happened this week in AI by Louie While the discourse around AI (including our own!) is often dominated by language models and agents, this week brought a powerful reminder of another, perhaps more profound, revolution happening in parallel: the application of AI to the fundamental code of biology.
Dr. Ravi Gedela, CEO, Banking Labs Inc., an AI-powered financial intelligence company. Generative AI (GenAI) is no longer an emerging technology—it's here, it's powerful and it’s reshaping industries.
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.
Whether it’s streamlined ingestion, unified governance or AI agent evaluation, the suite of solutions available on the Data Intelligence Platform enables businesses worldwide to drive
Python’s automation tools that are powering everything from website testing and browser automation to streamlining desktop workflows and automating Python project testing.
Anysphere, the company behind the Cursor AI coding environment, issued an apology Friday regarding a poorly communicated pricing adjustment to its $20-per-month Pro plan, which led to unexpected costs for some users. Michael Truell, CEO of Anysphere, stated in a blog post, “We recognize that we didn’t handle this pricing rollout well and we’re sorry.
Local AI applications can greatly simplify everyday tasks, but they require an NPU to run smoothly. This article explains why and how you can use NPUs effectively.
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.
This is what happens when I stay home for too long 🤣 In this 25-minute video, we take a deeeeep dive into Arcee AI Fusion — a powerful new method for model merging that enables strong domain adaptation while preserving the base model’s original capabilities and knowledge. We start with a high-level overview of what Arcee Fusion is and why it matters.
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Ruoming Pang, Apple’s head of AI models, is reportedly departing the company to join Meta, according to a Monday report by Bloomberg. This recruitment marks the latest addition of a high-ranking artificial intelligence executive to Meta CEO Mark Zuckerberg’s new AI superintelligence unit. Pang previously led Apple’s internal team responsible for training the AI foundation models.
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 How scientists are trying to use AI to unlock the human mind Understanding the mind is hard. Understanding AI isn’t much easier. By Grace Huckins archive page July 8, 2025 Stephanie Arnett/MIT Technology Review | Adobe Stock Today’s AI landscape is defined by the ways in which neural networks are
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
Data flow diagrams (DFDs) are indispensable tools in the realm of business processes and software development. Their ability to visually map information flow makes them vital for understanding and improving systems. By providing a structured representation of how data moves within an organization or application, DFDs help stakeholders identify efficiencies, visualize processes, and communicate effectively.
LangChain, an AI infrastructure startup providing tools to build and monitor LLM-powered applications, is raising a new round of funding at an approximate $1 billion valuation led by IVP, according to three sources with knowledge of the deal.
Most businesses expect that artificial intelligence (AI) will save money and promote efficiency as it continues to be adopted and implemented across various industries. But what these companies may not expect – and AI cannot predict – are the myriad ways that AI will result in new and increasing potential for liability. From addiction-forming or defamatory chatbots to automated hiring tools with discriminatory impact, AI-driven technologies […] The post Insurance for AI Liabilities: An Evo
Artificial intelligence (AI) and large language models (LLMs), such as ChatGPT, are transforming how we learn. But what does this mean for AI and learning retention?
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.
Anthropic released news that its models have attempted to contact the police or take other action when they are asked to do something that might be illegal. The company’s also conducted some experiments in which Claude threatened to blackmail a user who was planning to turn it off. As far as I can tell, this kind of behavior has been limited to Anthropic’s alignment research and other researchers who have successfully replicated this behavior , in Claude and other models.
Opinion: Loeb & Loeb's Tal Dickstein analyzes what two major decisions that allowed tech companies to use copyrighted materials to train their LLMs mean for the future of artificial intelligence and intellectual property.
The scientific method is a powerful tool that has transformed how we understand the world. It allows us to make sense of complex phenomena and provides a structured approach to uncovering the unknown. By emphasizing observation, experimentation, and analysis, the scientific method fosters a deeper inquiry that spans across multiple disciplines. Let’s explore its essential components and applications.
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 7 Steps to Mastering Vibe Coding Learn how to master vibe coding in these 7 steps, and transform AI code generation into a professional superpower.
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
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