This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
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 Building End-to-End Data Pipelines: From Data Ingestion to Analysis Check out this practical guide to designing scalable, reliable, and insight-driven data infrastructure.
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
If you’ve been following developments in open-source LLMs, you’ve probably heard the name Kimi K2 pop up a lot lately. Released by Moonshot AI , this new model is making a strong case as one of the most capable open-source LLMs ever released. From coding and multi-step reasoning to tool use and agentic workflows, Kimi K2 delivers a level of performance and flexibility that puts it in serious competition with proprietary giants like GPT-4.1 and Claude Opus 4.
This post was co-written with Mohammad Jama, Yun Kim, and Barry Eom from Datadog. The emergence of generative AI agents in recent years has transformed the AI landscape, driven by advances in large language models (LLMs) and natural language processing (NLP). The focus is shifting from simple AI assistants to Agentic AI systems that can think, iterate, and take actions to solve complex tasks.
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.
Summary: ReLU in deep learning helps models learn faster by passing positive values and turning negatives into zero. It’s simple, efficient, and widely used. Learn how to implement the ReLU activation function in Python and why it’s preferred over older methods in AI and machine learning. Introduction If you’ve ever wondered how machines learn to recognize faces, understand speech, or play games better than humans, you’re not alone.
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 Sponsored Humans and technology Finding value with AI automation Rules-based policies and processes offer opportunities for enterprises to begin successful AI automation journeys.
We revisit the problem of secure aggregation of high-dimensional vectors in a two-server system such as Prio. These systems are typically used to aggregate vectors such as gradients in private federated learning, where the aggregate itself is protected via noise addition to ensure differential privacy. Existing approaches require communication scaling with the dimensionality, and thus limit the dimensionality of vectors one can efficiently process in this setup.
Sign up to get articles personalized to your interests!
Data Science Current brings together the best content for data science professionals from the widest variety of thought leaders.
We revisit the problem of secure aggregation of high-dimensional vectors in a two-server system such as Prio. These systems are typically used to aggregate vectors such as gradients in private federated learning, where the aggregate itself is protected via noise addition to ensure differential privacy. Existing approaches require communication scaling with the dimensionality, and thus limit the dimensionality of vectors one can efficiently process in this setup.
This is a guest post co-written with Rahul Ghosh, Sandeep Kumar Veerlapati, Rahmat Khan, and Mudit Chopra from PayU. PayU offers a full-stack digital financial services system that serves the financial needs of merchants, banks, and consumers through technology. As a Central Bank-regulated financial institution in India, we recently observed a surge in our employees’ interest in using public generative AI assistants.
Natural language is emerging as the cornerstone of modern AI agent development, transforming how we conceptualize, build, and deploy intelligent systems. Unlike traditional software development that requires extensive programming knowledge, AI agents operate through the same conversational medium that humans use naturally–making agent development accessible to domain experts who understand business problems but may lack technical coding skills.
New research demonstrates methods for reducing AI energy consumption by up to 90 percent. New methods for shrinking the large language models (LLM) that power most AI apps can slash their energy consumption by up to 90 percent, according to a new study released this month.
Sitemap Open in app Sign up Sign in Medium Logo Write Sign up Sign in Member-only story Harvard and MIT Study: AI Models Are Not Ready to Make Scientific Discoveries AI can predict the sun will rise again tomorrow, but it can’t tell you why Alberto Romero 20 min read · Just now -- Share Source A study by researchers at Harvard and MIT sheds light on one of the key questions about large language models (LLMs) and their potential as a path to artificial general intelligence (AGI): Can foundation A
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 post is co-written with Kshitiz Gupta, Wenhan Tan, Arun Raman, Jiahong Liu, and Eiluth Triana Isaza from NVIDIA. As large language models (LLMs) and generative AI applications become increasingly prevalent, the demand for efficient, scalable, and low-latency inference solutions has grown. Traditional inference systems often struggle to meet these demands, especially in distributed, multi-node environments.
When you hear "python vs. anaconda" you might think of a clash between two massive snakes, but it’s also a fair comparison between a powerful programming language and a robust data science distribution. Whether you’re talking snakes or software, the differences are fascinating.
When we launched the AWS Generative AI Innovation Center in 2023, we had one clear goal: help customers turn AI potential into real business value. We’ve already guided thousands of customers across industries from financial services to healthcare—including Formula 1, FOX, GovTech Singapore, Itaú Unibanco, Nasdaq, NFL, RyanAir, and S&P Global—from AI experimentation to full-scale deployment, driving millions of dollars in productivity gains and transforming customer experiences.
A new research article from Stanford University reveals that leading AI models, including OpenAI’s GPT-4o and Meta’s LLaMA 3, often provide responses that contradict established therapeutic guidelines, with some answers posing significant risks to users. ISTANBUL, TR – In a significant evaluation of artificial intelligence’s role in mental healthcare, a new study from Stanford University has found that popular AI chatbots are frequently at odds with best practices in therapy.
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.
Apple’s MLX machine learning framework, originally designed for Apple Silicon, is getting a CUDA backend, which is a pretty big deal. Here’s why. The work is being led by developer @zcbenz on GitHub (via AppleInsider), who started prototyping CUDA support a few months ago.
Highly skilled employees leave a company. This move happens so suddenly that employee attrition becomes an expensive and disruptive affair too hot to handle for the company. Why? It takes a lot of time and money to hire and train a complete outsider with the company’s nuances. Looking at this scenario, a question always arises […] The post Why Are Employees Leaving?
Glassdoor and Indeed just announced about 1,300 employees being laid off. Let that fully sink in. The companies we look to as the gatekeepers of the job market, to help us find work and enable us to recover from layoffs, are themselves suffering the effects of automation.
This post is co-written with Andrew Liu, Chelsea Isaac, Zoey Zhang, and Charlie Huang from NVIDIA. DGX Cloud on Amazon Web Services (AWS) represents a significant leap forward in democratizing access to high-performance AI infrastructure. By combining NVIDIA GPU expertise with AWS scalable cloud services, organizations can accelerate their time-to-train, reduce operational complexity, and unlock new business opportunities.
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.
Notes on the recent trend of “Hire and License Out” deals in AI Over the last year, a new breed of deal structure has emerged in AI: an alternative to acquisitions and hiring that shares traits of both yet isn’t quite either.
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
Bain & Company has entered into a strategic partnership with AI Aspire, the advisory firm founded by AI researcher and entrepreneur Andrew Ng, to help global enterprises scale artificial intelligence across their operations.
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
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
Pradeep Kumar Muthukamatchi is a Principal Cloud Solution Architect at Microsoft and a passionate advisor to numerous startups. I've had a front-row seat, guiding countless startups as they harness the immense power of cloud and AI. Every day, I witness startups achieving remarkable feats with AI.
Device makers and wireless carriers are taking necessary steps to improve satellite messaging, though there is room for improvement in handovers and other functions.
AI researchers from OpenAI, Google DeepMind, Anthropic, and a broad coalition of companies and nonprofit groups, are calling for deeper investigation into techniques for monitoring the so-called thoughts of AI reasoning models in a position paper published Tuesday.
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.
This article provides a clear, practical guide to help you choose a niche, find clients, and scale your business effectively. Whether you're just starting out or looking to grow, you'll find actionable steps to succeed.
Think about an AI that doesn’t just guess what you’re going to do next, but knows it, can predict what you’ll do in new situations, can adapt to your …
Today, C‑Gen.AI came out of stealth mode to introduce an infrastructure platform engineered that the company said addresses a problem undermining AI’s potential: the inefficiency and rigidity of current AI infrastructure stacks.
Context-Generic Programming Overview Hire Me Contribute Resources Blog Book GitHub Recap Series Overview Extending the Visitor Pattern - Limitations of the Traditional Visitor Pattern - The Expression Problem Evaluator Computer - Evaluating Concrete Expressions - Dispatching Eval Converting to a Lisp Expression - Why Lisp? - The ComputerRef Component - Implementing PlusToLisp - Constructing Variants with Sub-Enums - Implementing LiteralToLisp Advanced Techniques - Binary Operator Provider - Code
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
We organize all of the trending information in your field so you don't have to. Join 17,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content